design and evaluation of led illumination source for...

Design and Evaluation of LED Illumination Source for

Multispectral Imaging Applications

A PROJECT REPORT

Submitted by

ANITA MARY PETER

in partial fulfillment for the award of the degree

0f

MASTER OF TECHNOLOGY

IN

OPTOELECTRONICS AND LASER TECHNOLOGY

INTERNATIONAL SCHOOL OF PHOTONICS

COCHIN UNIVERSITY OF SCIENCE AND TECHNOLOGY

COCHIN 682022

June-2015

INTERNATIONAL SCHOOL OF PHOTONICS

COCHIN UNIVERSITY OF SCIENCE AND TECHNOLOGY

CERTIFICATE

This is to certify that the Project Work Report entitled ‘Design and Evaluation of

LED Illumination Source for Multispectral Imaging Applications’ submitted by

Ms Anita Mary Peter (Reg No: 95713000), is the record of bonafide work carried out

by her under the guidance of Dr. Samuel Varghese at R&D SFO Technologies in partial

fulfillment of the requirements for the award of degree of Master of Technology in

Optoelectronics & Laser Technology from International School of Photonics, Cochin

University of Science and Technology during the period from July 2014 – June 2015.

Supervising Guide Director

Dr. Samuel Varghese Dr M. Kailasnath

Project Manager International School of Photonics

SFO Technologies Pvt. Ltd. CUSAT

ABSTRACT

Multispectral imaging, which extends the number of imaging channels beyond the conventional three, has demonstrated to be beneficial for a wide range of applications. Its ability of acquiring images beyond the visible range and applicability in many different application domains lead to the design and the development of a number of multispectral imaging technologies and systems. Among these systems Light Emitting Diode (LED) illumination based multispectral imaging (LEDMSI) has received much attention in recent years due to its fast computer controlled switching ability, robustness and cost effectiveness. Scene illumination is important to successful spectral imaging. This is an often-overlooked issue and is one of the main reasons for measurement performance being compromised.Given different modes of illumination to choose from, it is important to be able to compare them in a general and in many situations specific to a certain application of interest. In this paper, we evaluate two of the important types of illumination for the rapidly growing LED based multispectral imaging systems This work provides a framework for the evaluation of ring illumination for LED based multispectral imaging systems with different ring diameters and varying number of light emitting diodes for uniform illumination, which, would be very helpful in identifying the most appropriate technique or system for a given application.

CONTENTS

Chapter:1

Introduction…………………………………………………………………………….…..1

1.1 Objective and Scope of Project………………………............…….……….…..3

1.2 Thesis Outline…………………………………………………............……..………..3

Chapter:2

Literature Survey……………………….…………………………………………….….5

2.1 Introduction……………………………………………………….............……...……5

2.2 Multispectral Imaging System……………………………............…........…....7

2.3 Multispectral Imaging methods and approaches used…...........….....9

2.4 LED Illumination Based MSI System..........................................................12

Chapter:3

System Description......................................………………………...…………....14

3.1 Linear Fiber Coupled Illumination……………………….............………............14

3.2 Ring Illumination……………………………………………….....................................15

3.3 Wavelength Selection……………………………………................................………16

3.4 Selection of collimating lenses……….............………………………..............…..18

3.5 Filters……………………………………………………………..........................................18

3.6 Dichroic Beamsplitters…………………………………….............…….....................19

3.7 Final Source Structure……………………………………….............……..................20

Chapter:4

Optical Simulation and Evaluation........……….………………………………22

4.1 Linear Illumination Model………………..............………………………................22

4.2 Ring Illumination……………………………………………...............……...................30

4.3 Results and Discussion............................................................................................52

Chapter:5

System Design and Evaluation ……….........………………….………………...54

5.1 Circuit Design...................................………………...........……………………………..54

5.2 Switching Circuit........................................................................................................58

5.3 Experimental Setup...................................................................................................59

Chapter:6

Applications……………………………………………….........................................66

6.1 Estimation of Blood Analytes for Non Invasive Tissue Analysis....66

Chapter:7

Conclusion and Future Scope....................................................................77

References..............................................................................................................78

ACKNOWLEDGEMENT

I would like to express my gratitude and appreciation to all those who gave me

the possibility to complete this report. My thanks are due to the Director Dr. M

Kailasnath and International School of Photonics, CUSAT for having provided all facilities

that helped to proceed in this mission.

I take this opportunity to place on record my indebtedness to Dr. Suresh Nair,

Chief Technology Officer, R&D, SFO Technologies Pvt Ltd for giving me the opportunity

to carry out this investigation at SFO Technologies.

I express my sincerest gratitude to my project guide, Dr Samuel Varghese,

Project Manager R&D SFO Technologies whose constant support and guidance from the

very beginning through all stages till date is the very soul of this project.

I would also like to acknowledge with much appreciation the crucial role of the

team of engineers at SFO Technologies, especially Mr Robin, Mr. Siju Lal, Ms Jinnu Jose

and all others for their timely help and support.

I express my warm thanks to all the teachers and staff at CUSAT especially Prof

Radhakrishnan for their constant support and appreciation.

And finally, I thank my family, friends and the one above all of us, the

omnipresent god, for all blessings, for giving me the strength and support throughout.

Design and Evaluation of LED Illumination Source for Multispectral Imaging Applications

International School of Photonics 1

Chapter 1 Introduction

Multispectral imaging (MSI) is the synergistic combination of imaging and spectroscopy. Spectroscopy is the technique of breaking light down into its composite colors to identify the composition of the object. Multispectral images are data-rich, revealing things beyond our human vision by combining ultraviolet (UV) fluorescence, narrow-band color and penetrating near-infrared (NIR) images.

Multispectral Imaging(MSI), originated from remote sensing and has been explored for various applications by NASA. The use of imaging techniques for scene evaluation has become a key method for nondestructive, remote evaluation. Conventional imaging, whether monochrome or color, depends upon the spatial resolution of the image. If the image is blurred or too fuzzy then the viewer cannot determine exactly where or what the object under view is. Multispectral imaging adds significant additional information to the brightness analysis of a scene by adding the light intensity as a function of wavelength, its spectrum, from each image’s spatial position. This additional spectral dimension can be rapidly and straightforwardly analyzed to provide an image-contrast capability that is not present in a normal scene-brightness approach. In the early 1970s, the remote sensing satellite, LandSat I, was the first to provide multispectral images of the Earth's surface using reflectance spectroscopy. With the advantage of acquiring two-dimensional images across a wide range of electromagnetic spectrum, Multispectral or Hyperspectral Imaging has been applied to numerous areas, including archaeology and art conservation, vegetation and water resource control, food quality and safety control, forensic medicine, crime scene detection, biomedicine, etc. As an emerging imaging modality for medical applications, it offers great potential for noninvasive disease diagnosis and surgical guidance.



Scene illumination is important to successful spectral imaging. This is an often-overlooked issue and is one of the main reasons for measurement performance being compromised. Simply said, if one cannot get any light or enough light into the imaging system, it doesn't matter how good the system is spectrally, spatially or otherwise; it will not measure what is required by the user. The efficient delivery of light of required wavelength is an important aspect of multispectral imaging. The optical and spectral characteristics of a multispectral imaging system are determined largely by the application requirements. Light Emitting Diode (LED) illumination based multispectral imaging (LEDMSI) has received much attention in recent years due to its fast computer controlled switching ability, robustness and cost effectiveness. The primary hindrance in using LEDs as an illumination source for multispectral imaging was the limited wavelengths and comparatively very low intensity. In recent times, the availability of many different colour and high intensity LEDs with peak wavelengths spanning the whole visual range and even infrared and ultraviolet region has made the construction of more effective multispectral imaging systems possible and with little effort. In a typical LEDMSI system a set of N different types of LED are selected, each colour LED is illuminated in a sequence and a camera captures images under the illuminated LEDs one at a time, thus producing a N band multispectral image. Such a system modulates the illumination and provides a multispectral light source. LEDMSI can be used in several applications like biometrics, medical bioimaging, film scanner and cultural heritage. After a basic study into the general aspect of multispectral imaging and its approaches we will focus into the LED multispectral imaging system specifically to the illumination part. Given different illumination types, it is important to evaluate and compare the performance and the quality of these systems. This is useful in identifying a suitable system to be used for a given application and its requirement.



1.1 Objective and Scope of project

The objective of this project is study and design an LED based source for a simple and low cost Multispectral Imaging System for applications mainly in the biomedical field.

The objective is

To design and simulate the two sources – a ring source and a fiber source - using optical simulation software zemax

Characterize the source properties with respect to the following factors • Effect of properties of light emitting diodes on the system performance • Effect of ring diameter for uniform illumination

Hardware implementation of the ring illumination source Develop a prototype of a portable multispectral imaging system with ring

illumination and sequential switching for instantaneous imaging at multiple wavelengths.

To evaluate the possible field of application for the illumination systems.

Illumination using LEDs can greatly reduce the size and weight of the MSI system and ensures higher efficiency. It also provides increased lifetime and low cost systems which can also be portable.

1.2 Thesis Outline

Multispectral imaging (MSI) is the synergistic combination of imaging and spectroscopy. Spectroscopy is the technique of breaking light down into its composite colors to identify the composition of the object. Multispectral images are data-rich, revealing things beyond our human vision by combining ultraviolet (UV) fluorescence, narrow-band color and penetrating near-infrared (NIR) images.

Chapter 1 provides an introduction to the concept of LED based multispectral imaging and its possible applications. It also lists out the objectives and the scope of the project.



The objective of this project is to study two types of configuration using LEDs for the illumination of a Multispectral Imaging System, one proposed as a linear fiber coupled delivery system and the other as a ring illumination system.

Illumination using LEDs can greatly reduce the size and weight of the MSI system and ensures higher efficiency. It also provides increased lifetime and low cost systems which can also be portable.

Chapter 2 comprises the literature survey on multispectral imaging and the different types of existing multispectral imaging systems.

Chapter 3 deals with the proposed system description.

Chapter 4 details the simulation of the sources using the optical simulation software Zemax. The led source parameters are varied and the results are determined using zeemax.

Chapter 5 consists of the system design for the system, the circuit design and experimental setup. The power supply, led driver circuit and switching circuit design are detailed in this chapter.

Chapter 6 explains an application 2D estimate of blood analytes using the 680, 780 and 800nm wavelengths.

Chapter 7 is the conclusion and possible future developments.



Chapter 2 Literature Survey

2.1 Introduction

Multispectral imaging (MSI) is the synergistic combination of imaging and spectroscopy. Spectroscopy is the technique of breaking light down into its composite colors to identify the composition of the object. Multispectral images carry information about a number of spectral bands: from three components per pixel for colour images to several hundreds of bands for hyperspectral images. MSI can obtain a high-resolution optical spectrum for each image pixel, resulting in a series of images of the same field of view that are acquired at different wavelengths.

The power of multispectral imaging is already leveraged in a wide variety of research applications. Multispectral images are data-rich, revealing things beyond our human vision by combining ultraviolet (UV) fluorescence, narrow-band color and penetrating near-infrared (NIR) images. However, until recently, there has not been a feasible way to scale this technology for production-volume portable devices or high-speed inspection in specific commercial and industrial applications.

Fig 2.1.1: Multispectral Image



There are basically two main types of spectral imaging techniques in use currently : hyperspectral imaging and multispectral imaging. Hyperspectral imaging (HSI), which acquires spectral images in a large number of narrow spectral bands, produces high spectral accuracy. However, the acquisition time, complexity and cost of these systems are generally quite high compared to multispectral systems. Multispectral imaging (MSI) on the other hand acquires images in a limited number of relatively wide spectral bands, and the spectral reflectance functions are obtained from the sensor responses using an estimation algorithm. Multispectral imaging provides cheaper and faster solutions compared to hyperspectral imaging with good enough quality for many applications. The table below shows the different modes of imaging that exist currently and that which are proposed to exist in the future.

Table 2.1 Different Modes of Imaging

Mode

Number of Spectral

Bands

Spectral Resolution

Availability

Imaging

None

None, sensitivity depends on

detector spectral response

Now

Multispectral

Few to tens

Medium, many tens of nm

Now

Hyperspectral

Hundreds to ~thousands

Narrow, few nm Now

Ultraspectral

Thousands

Very narrow

Emerging technology but very expensive

and processor hungry

Full spectrum

Full spectrum Thousands to

"continuous spectra" over full optical

spectral range from UV to IR

Very narrow

Proposed technology

and data processing system



Advantages of Multispectral Imaging

Multispectral imaging has many advantages over traditional three channel (usually RGB) color imaging.

• It is less prone to metamerism. • Produces higher color accuracy • Unlike digital cameras, it is not limited to the visual range, rather can also be

used in near infrared, infrared and ultraviolet spectrum as well, depending on the sensor responsivity range.

• Spectral reflectance of a scene which represents the unique property of an object, can be recovered from the images acquired with the spectral imaging systems.

2.2 Multispectral Imaging System

Multispectral imaging systems are developing rapidly because of their strong potential in many domains of application, such as remote sensing, astronomy, physics, museum, cosmetics, medicine, high-accuracy colour printing, computer graphics...

A multispectral imaging system can be separated into two sections

Illumination System and

Image Acquisition System

Illumination System

The illumination system is used for illuminating the sample. Typically it consist of a light source which can be a broadband source such as mercury or halogen lamp or a narrow band source such as laser diode or light emitting diode.

It also consist of filters for selecting particular wavelengths and collimation optics to get a collimated beam and to reduce divergence in case of illuminating a small sample. In order to combine multiple wavelengths dichroic beamsplitters can be used. The delivery of light can be direct or through a fiber bundle depending on the application.



Image Acquisition System

The image acquisition system means the mechanism for capturing the multispectral image. It typically consists of a monochrome or RGB camera. Filter wheels or electronically tuned filters can also be used in front of the camera to select appropriate wavelength.

The output of the image acquisition system can be spectral images at single wavelength or a combination of wavelengths which are to be processed using relevant processing software to extract the relevant information.

A synchronizing system can be used in case of sequential illumination which synchronizes the illumination system with that of image acquisition.

Fig 2.2.1 Sections of Multispectral Imaging System



2.3 Multispectral imaging methods and approaches used In this section, briefly described are the conventional systems, and the three new promising fast and practical multispectral imaging systems: stereo camera (StereoMSI), filter array (FAMSI), and LEDMSI of we focus into the illumination part of the LED Multispectral Imaging System.

2.3.1 Filter wheel based MSI (FWMSI) system: The conventional approach to acquiring multispectral imaging data is to use a white light source, such as a halogen or mercury-xenon lamp, and band-pass filters to select appropriate wavelengths. Filter wheels are typically used to provide switching of filters, with the wheel generating a series of triggers that instruct a camera when to capture each frame. In a typical FWMSI system, there are two possibilities such as filter wheel in front of the camera and filter wheel at the source end. Camera with Filter wheel: Here, n number of images of a scene is acquired by a monochrome camera, with each of the filters in the rotating filter wheel placed in front of the camera sensor or the lens of the camera. It acquires an n-band image in n exposures.

Fig 2.3.1 Monochrome camera with filter wheel



Filters at Source: The filter wheel is placed in front of the broadband source in this case. Only light of the required wavelength selected by the bandpass filters illuminates the scene and image is captured by a synchronized camera. The spectrum coverage of the filter wheel based MSI is limited by the filters used. Also the system speed is low because of the time taken to rotate the filter wheel and hence it is not suitable for images in motion. The cameras are not able to acquire images at their maximum frame rate. Another major disadvantage is that it requires moving parts. As concerned with the White Light Broadband Source, it does not have uniform spectral density, such that certain wavelengths may be less powerful than others. Also there is the danger of sample heating and phototoxicity from high intensity broadband illumination. For applications where scenes are static and where accuracy is more important but not the speed, classic FWMSI systems could be used. 2.3.2 Tuned Filter based MSI (TFMSI) system

In a typical TFMSI system, n number of images of a scene is captured by a monochrome camera in n exposures, one each with a narrow (or somewhat wide) band filter activated with a tunable filter (TF). Electronic tunable filters such as an acousto-optic tunable filter and liquid crystal tunable filters (LCTFs) are quiet, fast, compact, and stable and demonstrate increased spectral selectivity, spectral purity, and flexibility. Liquid crystal tunable filters (LCTFs) are optical filters that use electronically controlled liquid crystal (LC) elements to transmit a selectable wavelength of light and exclude others. LCTFs are often used in multispectral imaging because of their high image quality and rapid tuning over a broad spectral range. Another type of solid-state tunable filter is the Acousto Optic Tunable Filter (AOTF), based on the principles of the acousto-optic modulator. In comparison to LCTFs, AOTFs enjoy a much faster tuning speed (microseconds versus milliseconds) and broader wavelength ranges. However, since they rely on the acousto-optic effect of sound waves to diffract and shift the frequency

http://en.wikipedia.org/wiki/Liquid_crystal

http://en.wikipedia.org/wiki/Wavelength_of_light

http://en.wikipedia.org/wiki/Multispectral_imaging

http://en.wikipedia.org/wiki/Acousto-optic_modulator

http://en.wikipedia.org/wiki/Acousto-optic_modulator

http://en.wikipedia.org/wiki/Acousto-optic_effect



of light, imaging quality is comparatively poor, and the optical design requirements are more stringent. The disadvantages include low light throughput, increased cost and complexity of system and inappropriate capture if significant sample or camera movement occurs during the acquisition

Fig 2.3.2 Liquid crystal tunable filter and acousto optic tunable filter.

2.3.3 Stereo camera based MSI (StereoMSI) system This is a single shot StereoMSI system. The system is built from a digital stereo camera and two optimal filters, placed in front of the two lenses of the camera. The challenge with this system is the occlusion and the registration of the images from the two cameras. The use of stereo camera increases the system cost and complexity and the pixel by pixel registration of an image is rarely possible. By limiting its use to flat surfaces such as paintings, and with the use of an appropriate image registration technique, the issues could be mitigated reasonably well. It also has the limitation with the number of wavelengths that can be used.



2.3.4 Filter array based MSI (FAMSI) system FAMSI systems extend the trichromatic color filter array (CFA) employed in the conventional digital color imaging to n-bands. With the filter array technique, the filter is mounted on a common image sensor. This also allows single shot acquisition of a multispectral image. However, these systems require demosaicing in order to estimate missing band values in every pixel.

Fig 2.3.3 Filter array based sensor and demonaiced images

2.4 LED illumination based MSI (LEDMSI) system:

Unlike the previous systems, the LEDMSI is based on active illumination, where images are captured by illuminating the scene with n different predefined narrow band color LED light sources. In a typical LEDMSI system a set of N different types of LED are selected, each colour LED is illuminated in a sequence and a camera captures images



under the illuminated LEDs one at a time, thus producing a N band multispectral image. Such a system modulates the illumination and provides a multispectral light source. LEDMSI is considered a fast way of multispectral imaging, as the whole process can be controlled electronically. The system speed is limited only by the maximum frame rate of the camera. LED illumination based multispectral imaging can be used in several applications like biometrics, medical imaging and film Scanner. This has got much attention in recent years because of the advantages of the LEDs: fast computer controlled switching ability, robustness, and cost effectiveness.

Fig:2.4.1 LED Source for multispectral imaging system



Chapter 3

System Description

In recent times, the availability of light emitting diodes in a wide variety of wavelengths from ultraviolet to infrared, and optical power ranging from few milliwatts to hundreds of hundreds of milliwatts, it becomes a very viable solution to use light emitting diodes for illumination in multispectral imaging.

Here two types of illumination methods are discussed a linear fiber illumination which has primary application in endoscopy and a ring illumination with more focus given to the ring illumination system with detailed study of the illumination characteristics with respect to the viewing angle, diameter of the ring and height of illumination.

3.1 Linear Fiber Coupled Illumination

Here, the light from the different LEDs are made to traverse to a single path through different optics and then coupled to a fiber.

The light from different leds are combined to a single path using wavelength selective dichroic beamsplitters. The number of beamsplitters used depend upon the number of wavelengths to be combined. Before the beamsplitter the light from the leds should be collimated using suitable lenses so as to minimize the loss of light and to ensure efficient delivery and coupling to fiber.

The use of filters is not a necessity. It is the choice of the user depending upon the application. Usually leds provide a spectral bandwith of around a few tens of nanometres. A filter can be used if there is need for very narrow spectral bandwidth in



the range of nanometres. Finally the light is coupled to a fiber bundle using focussing lenses.

The basic structure for the source is

Fig 3.1 Basic source structure of linear fiber coupled led illumination

3.2 Ring Illumination

The ring illumination is practically the simplest type of illumination that can be implemented and with minimum cost because it does not require any additional collimating or focussing optics. The leds are usually arranged in a single ring or in more than one concentric rings. The only requirement is to arrange the different types of leds so that they provide nearly uniform illumination with the required power at a specified distance. The number of leds and size of ring depends on the application and the size of the camera since the camera is placed at the center and the leds around as a ring and the name ring illumination. The importance of ring illumination is the presence of camera at the center so that perpendicular image can be obtained with equal intensity from all pixels and correct dimension.



The basic source structure is

Fig 3.2 Basic structure for ring illumination

3.3 Wavelength selection

A key parameter of any multispectral system is the definition of the set of discrete wavelengths selected to illuminate the surface under inspection. This set of wavelengths is specific for each problem, as it depends strongly in the diffuse reflectance characteristics of the surface in regard to the application under consideration.

The number of bands and the wavelengths used depend entirely on the application. It can vary from three to tens of wavelengths.

A visible (VIS) and near-infrared (NIR) range are desirable in many applications. The VIS range is from 380 to 780 nm. The components are reproduced as grayscale images or an RGB color image.

The NIR range is from 780 to 2500 nm. The components are reproduced as grayscale images or any three components can be taken and used as R,G,B components. i.e. pseudocolor reproduction. The NIR light penetrate deeper in materials, e.g. paint or tissue, and may show information invisible by a human eye.

For fluorescent multispectral imaging the wavelength which fluoresce the specific chromophore may be used. Usually wavelengths in UV and blue region produce much of the natural fluorescence. In addition, the contrast agent may be injected and



propagated into blood or lymphatic vessels. For example, idocyanine green gives a fluorescent light at 830 nm after the exciting illumination at 780 nm. The fluorescent light highlights the vessels in the NIR image. This is used for analysis in medicine.

For the present analysis we use three wavelengths in red near infrared region. The primary field of application for these wavelengths is in bioimaging specifically skin image analysis. This is proposed in view of the wide range of applications and possible future aspects of skin imaging.The selected wavelengths are 680nm, 780nm and 800nm.

Three high power leds of the desired wavelengths is selected for fiber coupled illumination system since only a single led is used. For the ring type illumination mode low power leds of the above wavelengths each having different beam angles are selected so as to study how properties of leds affect the source and optimize it.

Table 3.3.1 LED Specification – Fiber Coupled Illumination

Required Wavelength

(nm)

Available Wavelength

(nm)

Total Optical Power (mW)

Current (mA)

Voltage (V)

Half Viewing

Angle

680 680 130 600 2.5 62

780 780 290 800 2.5 63

800 810 320 800 2.3 66

Table 3.3.2 LED Specification – Ring Illumination

Required Wavelength

(nm)

Available Wavelength

(nm)

Total Optical Power (mW)

Current (mA)

Voltage (V)

Half Viewing

Angle

680 680 5.5 20 1.8 35

780 770 5.5 20 1.55 12

800 810 20 50 1.55 7



3.4 Selection of Collimating Lenses

Since the output of an LED is highly divergent, collimating optics are necessary.Collimation is required to minimize the loss of optical power in the path from the led to the fiber. A well-collimated beam has minimal divergence and will not converge at any point in the beam path.

Compared to spherical lenses, aspheric condenser lenses introduce less aberration, offer larger apertures, higher NA, and lower f/d Ratio

Hence an aspheric condense lens is used to collimate the light from each LED.

Specifications ■ Diameter : 25mm ■ Clear Aperture : 22.5mm ■ Focal Length : 20mm ■ Focal Length Tolerance: 5% ■ Surface Quality: 60-40 Scratch-Dig ■ Glass: B270 Optical Crown Glass Fig 3.4 Aspheric condenser lens 3.5 Filters The use of filters is optional. The light from the LED has a spectral bandwidth of few tens of nanometres. If a very narrow spectral bandwidth is required for specific application filters can be used. Three hard coated bandpass filters of the required wavelength as above are selected. These hard coated bandpass filters provide better transmission, steeper cut on and cut off slopes, greater blocking, and increased durability when compared to standard line of bandpass filters

http://www.thorlabs.com/navigation.cfm?guide_id=2210



Table 3.5.1 Filter Specification

Center Wave-length

Blocking Region

FWHM

Diameter

Clear

Aperture

Mounted Thickness

Substrate Thickness

680nm 200 - 673 nm, 686 - 1200 nm

13nm 25mm 21.5mm 5mm 2mm

780nm 200 - 752 nm, 808 - 1200 nm

10nm 25mm 21.5mm 3.5mm 2mm

800nm 200 - 771 nm, 829 - 1200 nm

10nm 25mm 21.5mm 3.5mm 2mm

3.6 Dichroic Beamsplitters

Dichroic mirrors/beamsplitters spectrally separate light by transmitting and reflecting light as a function of wavelength. Transmission and reflection are 50% at the cutoff wavelength. A longpass dichroic mirror is highly reflective below the cutoff wavelength and highly transmissive above it, while a shortpass dichroic mirror is highly transmissive below the cutoff wavelength and highly reflective above it. Dichroic mirrors/beamsplitters can be used to combine a beam that has a wavelength (or wavelength range) shorter than the cutoff wavelength with a beam that has a wavelength (or wavelength range) longer than the cutoff wavelength while minimizing intensity losses. For a 3 wavelength system two beam splitters are required to combine the wavelengths. The requirements for the two beam splitters are Beamsplitter 1 – Cutoff wavelength between 680 and 780nm Beamsplitter 2 – Cutoff wavelength between 780 and 800nm with very narrow

transition region



The two selected beamsplitters are Table 3.6.1 Beam splitter specification

Cutoff Wavelength

Type Transmission Band

Reflection Band

Substrate Thickness

791nm Shortpass Tavg > 90% 687 - 787 nm

Ravg > 90% 795 - 940 nm

1.05mm

757nm Longpass Tavg > 93% 768 - 1100 nm

Ravg > 98% 450 - 746 nm

1.05mm

3.7 Final Source Structure The final source structure was determined after considering all the component optics required. The source structure for fiber delivery is a bit complex and requires a number of components for collimating and combining beam to the required path. The optimum distance between the components for maximum efficiency is determined by simulating in zemax. The ring structure is rather simple. Leds of each wavelength were placed on the ring to provide a uniform illumination. The source can be studied by varying the number of leds and the diameter of the ring and then studying the performance of the system.



Fig 3.7 Final source structure for linear fiber coupled delivery system.



Chapter 4

Optical Simulation and Evaluation The two source structures were simulated using the optical ray tracing software Zemax. Zemax is an optical design program that is used to design and analyze illumination systems as well as, imaging systems such as camera lenses. It works by ray tracing—modelling the propagation of rays through an optical system. It can model the effect of optical elements such as simple lenses, aspheric lenses, gradient index lenses, mirrors, and diffractive optical elements, and can produce standard analysis diagrams such as spot diagrams and ray-fan plots. Zemax can also model the effect of optical coatings on the surfaces of components The simulation using sources can be done using the non sequential ray tracing model in the zemax. In non-sequential ray tracing the order of components mentioned is not the order in which computation takes place. The order of computation is determined by the rays on the go. The high power leds are modelled as radial sources. Radial sources provide the nearest approximation to the light emitted by the leds. And also since we are using wide angle LEDs without lenses, it is a very close approximation as a radial source. The relative radial intensity for each led can be obtained from the data sheet. 4.1 Linear Source Model In the linear source model, there are two important parts, the collimation of light by the aspheric condenser lenses and the wavelength selective reflection and transmission by the beamsplitter.

http://en.wikipedia.org/wiki/Optical_design

http://en.wikipedia.org/wiki/Camera_lens

http://en.wikipedia.org/wiki/Ray_tracing_%28physics%29

http://en.wikipedia.org/wiki/Ray_%28optics%29

http://en.wikipedia.org/wiki/Simple_lens

http://en.wikipedia.org/wiki/Aspheric_lens

http://en.wikipedia.org/wiki/Gradient_index_lens

http://en.wikipedia.org/wiki/Mirror

http://en.wikipedia.org/w/index.php?title=Diffractive_optical_element&action=edit&redlink=1

http://en.wikipedia.org/w/index.php?title=Spot_diagram&action=edit&redlink=1

http://en.wikipedia.org/w/index.php?title=Ray-fan_plot&action=edit&redlink=1

http://en.wikipedia.org/wiki/Optical_coating



The aspheric condenser lenses can be modelled as an even asphere lens in zemax. The radius of curvature, conic and aspheric coefficients are obtained from the data sheet. Diameter - 25mm Radius of curvature - 10.5mm Conic - -0.6

Fig 4.1.1 LED with aspheric lens NSC shaded model The filter can be modelled by using a cylindrical volume with dimensions of the filter. The optical properties are defined using a coating which carries the transmission data of the filter and also by defining the material of the substrate which is uv fused silica for a hardcoated bandpass filter. The most important part in the modelling is to model the dichroic beamsplitter. The dichroic beam splitter is made by dichroic coating on the reflective side and antireflection coating on all other sides. So the dichroic beamsplitter can be modelled by defining a coating which consist of the optical properties of the beamsplitter on the reflective side and defining an antireflection coating for all the other sides.



Fig 4.1.2 Source model for fiber illumination Non-sequential Editor

Fig 4.1.3 Source model for fiber illumination



Each led is switched on sequentially, hence it is better to view the path of each led independently and its intensity after two beamsplitters. The layout rays were chosen to be 1000 for a good preview and the analysis rays were chosen to be 10000000 for near perfect analysis. LED 780nm Optical Power Output of 780nm Led = 290mW

Fig 4.1.4 LED 780nm path Non-sequential component editor

Fig 4.1.5 LED 780nm path NSC Shaded Model



It is evident from viewing the ray pattern in the NSC shaded model that the rays coming out of the second beamsplitter is nearly uniform and it does not further undergo any deviation after the aspheric lens. The detector is placed at a distance of 10mm from the second beamsplitter to view the intensity pattern at that point.

Fig 4.1.6 Detector View of led 780nm after second beamsplitter The total power detected is 96.7 mW This shows a loss of around 193.3mW, most of which is lost because of the wide viewing angle of the LED.



LED 810nm Optical Power Output of 810nm Led = 320mW


Fig 4.1.8 LED 810nm path NSC Shaded Model



Fig 4.1.9 Detector View of led 810nm after second beamsplitter The total power detected is 56.2 mW Here the loss of power can be partly attributed to the presence of 800nm bandpass filter. LED 680nm Optical Power Output of 810nm Led = 130mW Half viewing angle = 62




Figure 4.1.11 LED 680nm path NSC Shaded Model

Figure 4.1.12 Detector View of led 680nm after second beamsplitter The total power detected is 44mW



4.2 Ring Illumination For ring illumination three leds of wavelength 680nm, 780nm and 800nm in the relatively lower power range is chosen. The three leds are selected with different viewing angles to study the effect of viewing angles for uniform illumination. 4.2.1 LED Modelling The three different leds have different physical and optical characteristics. Therefore the leds are modelled for efficient simulation of the characteristics. The three leds were modelled in Zemax giving a good replica of the illumination pattern of each led. Each led has different viewing angle which is the primary parameter influencing the illumination pattern, that was corrected by changing the led lens. The narrow viewing angle is the result of the lenses present in front of the led emitter.

680nm LED

(a) (b) (c) Fig4.2.1.1 680nm (a) Original LED (b) LED model (c) LED Emission



780nm LED

(a) (b) (c) Fig4.2.1.2 780nm (a) Original LED (b) LED model (c) LED Emission 800nm LED

(a) (b) (c) Fig4.2.1.2 810nm (a) Original LED (b) LED model (c) LED Emission



4.2.2 LED Ring Simulation LED Ring Characteristics with Different Ring Diameters The light emitting diodes are arranged in a ring with varying diameters. The number of leds are varied to study the change in power and the uniform illuminated area. Here uniform illuminated area is taken as area with power 10% deviation from the peak power detected. The uniform area is specified as side of a square. 680nm LED Power of each LED is 5.5mW. The viewing angle is 70 degrees. The detector plane is taken to be at a distance of 5cm. Ring Diameter – 1cm

(a) (b) (c)

Fig 4.2.2.1 680nm Illumination pattern for a 1cm diameter ring (a) 2 led (b) 3 led (c) 4 led

Table 4.2.2.1 680nm LED ring- 1cm diameter

No of leds 2 3 4

Peak power(uW) 233 347 467

Uniform Illuminated Square Area (cm) 1.4 1.4 1.4



Fig 4.2.2.2 Plot 680nm Led ring – 1cm diameter

Ring Diameter – 2 cm

(a) (b) (c) (d)

(e) (f) (g)

Fig 4.2.2.3 680nm Illumination pattern for a 2cm diameter ring (a) 2 led (b) 3 led (c) 4 led (d) 5 led (e) 6 led (f) 7 led (g) 8 led




No of leds 2 3 4 5 6 7 8

Peak power(uW) 213 321 427 535 641 748 860

Uniform Illuminated Square Area (cm) 1.5 1.5 1.5 1.5 1.5 1.5 1.5


Ring Diameter -3 cm

(a) (b) (c)



(d) (e) (f) (g)

Fig 4.2.2.5 680nm Illumination pattern for a 3cm diameter ring (a) 2 led (b) 3 led (c) 4 led (d) 5 led (e) 6 led (f) 7 led (g) 8 led Table 4.2.2.2 680nm LED ring- 3cm diameter

No of leds 2 3 4 5 6 7 8

Peak power(uW) 185 278 371 464 574 663 860

Uniform Illuminated Square Area (cm) 1.6 1.6 1.6 1.6 1.6 1.6 1.6




Ring Diameter -4 cm

(a) (b) (c) (d)

(e) (f) (g)



No of leds 2 3 4 5 6 7 8

Peak power(uW) 159 247 326 410 489 571 640

Uniform Illuminated Square Area (cm)

1.8 1.6 1.8 1.9 1.9 1.9 1.9




Ring Diameter -5 cm

(a) (b) (c) (d)

(e) (f) (g)





No of leds 2 3 4 5 6 7 8

Peak power(uW) 141 198 260 324 388 454 519 Uniform Illuminated Square Area (cm) 2.2 2.5 2.5 2.5 2.5 2.5


780nm LED Power of each LED is 5.5mW. The viewing angle is 24 degrees. The detector plane is taken to be at a distance of 5cm.



Ring Diameter – 1cm

(a) (b) (c)

Fig 4.2.2.11 780nm Illumination pattern for a 1cm diameter ring (a) 2 led (b) 3 led (c) 4 led Table 4.2.2.5 780nm LED ring- 1cm diameter

No of leds 2 3 4

Peak power(uW) 0.849 1.27 1.7

Uniform Illuminated Square Area (cm) 0.7 0.7 0.7





(a) (b) (c) (d)

(e) (f) (g) Fig 4.2.2.13 780nm Illumination pattern for a 2 cm diameter ring (a) 2 led (b) 3 led (c) 4 led (d) 5 led (e) 6 led (f) 7 led (g) 8 led Table 4.2.2.6 780nm LED ring- 2cm diameter

No of leds 2 3 4 5 6 7 8

Peak power(uW) 0.602 0.906 1.21 1.5 1.81 2.1 2.4


0.8 0.8 0.9 0.9 0.9 0.9 0.9





(a) (b) (c) (d)

(e) (f) (g)



Fig 4.2.2.15 780nm Illumination pattern for a 3cm diameter ring (a) 2 led (b) 3 led (c) 4 led (d) 5 led (e) 6 led (f) 7 led (g) 8 led Table 4.2.2.7 780nm LED ring- 3cm diameter

No of leds 2 3 4 5 6 7 8

Peak power(uW) 0.493 0.576 0.709 0.883 1.04 1.21 1.37

Uniform Illuminated Square Area (cm) 2.4 2.4 2.4 2.4





(a) (b) (c) (d)

(e) (f) (g) Fig 4.2.2.17 780nm Illumination pattern for a 4cm diameter ring (a) 2 led (b) 3 led (c) 4 led (d) 5 led (e) 6 led (f) 7 led (g) 8 led


No of leds 2 3 4 5 6 7 8

Peak power(uW) 0.473 0.517 0.59 0.648 0.764 0.9 1.37






(a) (b) (c) (d)

(e) (f) (g)





No of leds 2 3 4 5 6 7 8

Peak power(uW) 0.465 0.495 0.541 0.59 0.625 0.7 0.808



810nm LED Power of each LED is 20 mW. The viewing angle is 14 degrees. The detector plane is taken to be at a distance of 5cm.




(a) (b) (c)

Fig 4.2.2.21 810nm Illumination pattern for a 1 cm diameter ring (a) 2 led (b) 3 led (c) 4 led


No of leds 2 3 4

Peak power(uW) 5.3 7.7 10.3 Uniform Illuminated Square Area (cm) 0.7 0.7 0.7





(a) (b) (c) (d)

(e) (f) (g) Fig 4.2.2.23 810nm Illumination pattern for a 2 cm diameter ring (a) 2 led (b) 3 led (c) 4 led (d) 5 led (e) 6 led (f) 7 led (g) 8 led Table 4.2.2.11 780nm LED ring- 2cm diameter

No of leds 2 3 4 5 6 7 8

Peak power(uW) 4.2 5 5.9 7.1 9.5 12.1 13.6 Uniform Illuminated Square Area (cm) 1.1 1.2 1.2 1.2





(a) (b) (c) (d)

(e) (f) (g) Fig 4.2.2.25 810nm Illumination pattern for a 3cm diameter ring (a) 2 led (b) 3 led (c) 4 led (d) 5 led (e) 6 led (f) 7 led (g) 8 led




No of leds 2 3 4 5 6 7 8

Peak power(uW) 3.5 3.7 4.2 5 5.8 6.6 7.4 Uniform Illuminated Square Area (cm) 2.1 2.2 2.3



(a) (b) (c) (d)



(e) (f) (g)



No of leds 2 3 4 5 6 7 8

Peak power(uW) 3.9 3.97 4.27 4.43 4.8 5.4 6 Uniform Illuminated Square Area (cm)





(a) (b) (c) (d)

(e) (f) (g) Fig 4.2.2.29810nm Illumination pattern for a 5cm diameter ring (a) 2 led (b) 3 led (c) 4 led (d) 5 led (e) 6 led (f) 7 led (g) 8 led Table 4.2.2.14 780nm LED ring- 5cm diameter

No of leds 2 3 4 5 6 7 8

Peak power(uW) 3.9 3.94 3.96 4.21 4.42 4.56 4.87





4.3 Results and Discussion

The radiant intensity from the three types of leds at wavelengths 680nm, 780nm and 800nm were evaluated in rings of different diameter ranging from 1cm to 5cm. The number of leds in each ring was also varied and the resultant radiation pattern is shown in the graphs.

It is evident from the graphs that leds with greater viewing angle as in the case of the 680nm led, has a more uniform gradient spread.

The graph plotted for the number of leds against the uniform illumination area for different ring diameters shows that the uniformly illuminated area (here taken as 10% deviation from the peak detected power) is dependent on the size of the ring only and is independent of the number of LEDs as it can be seen that the uniform illuminated area remains fairly constant for a particular type of led at a specific ring diameter. The increase in the number of leds increases the total power that falls on the area.



It can be seen that for leds with very small viewing angle as seen for 780nm and 810nm led with viewing angle 24 degrees and 14 degrees respectively, as the ring diameter increases there is no uniform illumination in the center of the ring. This can be compensated by using concentric rings

Therefore it can be concluded that there is a tradeoff between the no of leds used and the viewing angle of the light emitting diode. For applications where illumination of a very small area with concentrated higher intensity is required smaller viewing angles can be used and it requires more number of light emitting diodes.

Another major concern is the size of the camera used for the multispectral illumination system as can be seen for viewing angle less than 24 degrees in the case of 780nm and 810nm led inorder to ensure uniform illumination the size of the camera at the center of the ring should be less than 3cm.



Chapter 5

System Design and Evaluation 5.1 Circuit Design Circuit Design Section involves design of two major circuits for the system. The first one is the design of the LED driver circuit. The LEDs are sensitive devices. It is always necessary to limit the current or voltage below the maximum or else it will lead to burn out of the led if the current increases beyond limit. Also for efficient long term operation, the led driver circuit is essential. In addition a constant power supply must also be designed for the led driving circuit. The second one is the design of the switching and synchronizing circuit. The leds have to be switched sequentially in a definite time interval depending on the frame rate of the camera and the application. Hence a switching circuit should be designed to sequentially switch on the LEDs.

5.1.1 Driver Circuit Design The driver circuit is designed provide constant power to the LED even when the input power conditions vary.

There is also one other important requirement in our driver circuit to provide variation of led intensity from maximum to minimum. This can be done by varying the power to the LED within the maximum limit.

Here in this design ,we use 3 AL8807A for driving LEDs. The AL8807A is a step-down DC/DC converter designed to drive LEDs with a constant current. The device can drive up to 9 white high brightness LEDs in series from a voltage source of 6V to 36V. Figure below is the pin diagram of AL8807A.



Fig 5.1.1.1 Pin Diagram of AL8807A Features LED Driving Current up to 1A/1.3A Better than 5% Accuracy High Efficiency Up to 96% Optimally Controlled Switching Speeds Operating Input Voltage from 6V to 36V Wide Analog Input Range for Dimming Control (>10:1) Built-in Protection Features: Open-Circuit LED protection LED Chain Short Circuited Over-Temperature Protection AL8807A Operation

Fig 5.1.1.2 Typical Application Circuit of AL8807A



The AL8807A is a hysteretic LED current switching regulator sometimes known as an equal ripple switching regulator. In normal operation, when voltage is applied at +VIN (See Figure 31), the AL8807A internal switch is turned on. Current starts to flow through sense resistor R1, inductor L1, and the LEDs. The current ramps up linearly, and the ramp rate is determined by the input voltage +VIN, and the inductor L1. This rising current produces a voltage ramp across R1. The internal circuit of the AL8807A senses the voltage across R1 and applies a proportional voltage to the input of the internal comparator. When this voltage reaches an internally set upper threshold, the internal switch is turned off. The inductor current continues to flow through R1, L1, the LEDs and the schottky diode D1, and back to the supply rail, but it decays, with the rate of decay determined by the forward voltage drop of the LEDs and the schottky diode. This decaying current produces a falling voltage at R1, which is sensed by the AL8807A. A voltage proportional to the sense voltage across R1 is applied at the input of the internal comparator. When this voltage falls to the internally set lower threshold, the internal switch is turned on again. This switch-on-and-off cycle continues to provide the average LED current set by the sense resistor R1, with a switching current determined by the input voltage and LED chain voltage. Design – LED Current Control With the CTRL pin open circuit, the LED current is determined by the resistor, R1, connected between VIN and SET. The nominal average output current in the LED(s) is defined as: ILED = VTH R1 where VTH is nominally 100mV Resistor selection VTH = 100mV R1= VTH ILED



Diode Selection For maximum efficiency and performance, the rectifier (D1) should be a fast low capacitance Schottky diode with low reverse leakage at the maximum operating voltage and temperature. The Schottky diode also provides better efficiency than silicon PN diodes, due to a combination of lower forward voltage and reduced recovery time.It is important to select parts with a peak current rating above the peak coil current and a continuous current rating higher than the maximum output load current. In particular, it is recommended to have a diode voltage rating at least 15% higher than the operating voltage to ensure safe operation during the switching and a current rating at least 10% higher than the average diode current. The power rating is verified by calculating the power loss through the diode.Schottky diodes, e.g. B240 or B140, with their low forward voltage drop and fast reverse recovery, are the ideal choice for AL8807A applications. Inductor Selection Recommended inductor values for the AL8807A are in the range 33μH to 100μH.For this driver circuit it is decided to take 50 μH by considering input volage and number of LEDs in each string.

Fig 5.1.1.3 Driver Circuit



Power Supply A power supply capable of rendering 15V constant output is required for the ideal operation of the driver circuit since the input voltage is designd to be 15V. Circuit below shows a standard 15V power supply. The input can be taken from any 85- 265V AC power supply.

Figure 5.1.1.4 15V Power supply 5.2 Switching Circuit The switching circuit is designed so that instantaneous imaging of the three wavelengths is possible. This avoids the possible deviation in pixel position due to slight movement of the imaging portion. Here the switching circuit is designed with a time separation of 100ms. Each set of led is switched on for a period of 100ms. The timing is controlled by the clock signal given by the 555 timer circuit. The clock is designed for a total time period of 100ms.



The images can be captured by synchronising the camera with the switching of each led or by capturing in video mode and separating each individual frames.

Fig 5.2.1 Switchig Circuit 5.3 Experimental Setup The led ring was developed using 6 leds of each type to study the variation in illumination and confirm the simulation results. The astable multivibrator is designed with LM555 to produce 100ms pulses which are fed to the clock pin of decade counter. The decade counter produce output pulses at its output pins in a ripple fashion when each clock pulse comes. The first, third and fifth pins are connected to 3 different wavelength LEDs respectively. The image below shows the switching circuit implemented on breadboard for testing and the corresponding outputs of the clock circuit and the decade counter on the CRO screen.



Fig 5.3.1 Switching circuit set up and waveform while testing

(a) (b) Fig 5.3.2: CRO waveforms of 100ms pulse (a) astable and (b) decade counter

Fig 5.3.3: LED Ring and illumination



Fig 5.3.4 Experimental setup to measure the spatial distribution of led light. The uniformity and spatial distribution of the led light was measured using the power meter from thorlabs. The led ring is fixed and the detector is moved to obtain the light intensity at different points.

(a) (b) Fig 5.3.5 (a)Detector of power meter fixed on a movable stand (b) detected power displayed in the device



Fig 5.3.6 Measurement of radiant intensity Illumination Pattern Experimental and Simulation The ring structure was formed with 6 led each of all 3 wavelengths. The radiation intensity measured in experiment is compared with that of the simulation results.

(a)



(b) Fig5.3.7 Plot of radial power distribution 810nm led (a) experimental (b) simulation

(a)



(b) Fig5.3.8 Plot of radial power distribution 780nm led (a) experimental (b) simulation

(a)

0

50

100

150

200

250

300

350

400

-5

-4.5

-4

-3

.5

-3

-2.5

-2

-1

.5

-1

-0.5

0 0.

5 1 1.

5 2 2.

5 3 3.

5 4 4.

5 5

Radi

atio

n In

tens

ity (m

W)

Radial Distance



(b) Fig5.3.9 Plot of radial power distribution 680nm led (a) experimental (b) simulation The above plotted graphs show that the experimental results are in confirmation with the simulation results.



Chapter 6

Applications 6.1 Estimation of Blood Analytes for Non Invasive Tissue Analysis Under normal physiological conditions the cells in tissue will be supplied with the necessary oxygen, this is regulated through the blood supply and the oxygen saturation of the hemoglobin. Several physiological and patho-physiological processes may influence the blood flow to the skin. Non-invasive optical imaging techniques have been investigated to image skin lesions for mass screening to detect and analyse the morphological changes associated with tumorigenesis, thereby improving patient diagnosis accuracy. Deeper subsurface information, such as subcutaneous pigmentation and increased blood flow (angiogenesis) are critical factors in early stage skin cancers detection like melanoma. Non- invasive Multispectral imaging at visible and NIR wavelengths provides estimates of main chromophores in tissue.

Fig 6.1.1 :Schematic drawing of the skin with its blood vessels and a schematic drawing of the optical path of an incident beam



Figure 6.1.1 shows a schematic drawing of the blood vessels in the human skin. The skin is a complex multilayered organ which covers the whole body. The skin can be roughly divided into three layers: epidermis, dermis and the hypodermis. Interaction of light with skin tissue Light that enters the skin undergoes multiple internal reflections, scattering, and absorption events depending on the chromophores encountered. Attenuation of light in tissue is described, according to light transport theory, by the equation,

where I is the reflected light intensity, I0 the incident light intensity, μeff the effective attenuation coefficient and d is the optical path length in tissue. Main chromophores in blood tissue are Hb, HbO2, melanin etc. The light incident on the skin tissue eventually gets diffused across the layers of the skin and backscattered light photon energy forms an image of the skin and skin lesion. Figure below shows absorption spectra of main chromophores in tissue.

Fig 6.1.2 Absorption spectra of main chromophores, spotted values at 680nm, 780nm, 800nm in black Absorption of HbO2 and Hb varies depending on wavelength. As wavelength increases in the 680 nm to 800 nm range, absorption of Hb decreases while absorption of HbO2 increases. Melanin absorption is found maximum at 680nm.Thus, by comparing the



localized absorption difference between two or more wavelength ranges, we should obtain a measure how much spectral distortion is caused by blood absorption .The absorption rate of Hb, HbO2 can be used for estimation of haemoglobin content in blood which in turn results in diagnostics of different diseases such early stage cancer detection. Block diagram

Analysis based on the measurement of reflectance, at multiple specific wavelengths. Multiple LED sources operating at different wavelengths are sequentially excited. The reflected light intensity is detected using a single large area CMOS imager. This is repeated for each wavelength, within a time slot of milliseconds. Image processing is conducted in the PC using MATLAB. For the estimation of main chromophores present in the skin tissue ie oxyhemoglobin, deoxyhemoglobinand melanin by linear mixing model at least 3 wavelengths are required. In this proposed model 680nm,780nm and 800 nm LEDs are used for obtaining multispectral images of skin lesion. USB Camera is connected to a PC and operated in video mode. Using MATLAB, frames captured from the video. Distance between lens and target should be small as possible to get good image (approximately 5 cm for uniform illumination). A switching circuit



sequentially energized each wavelength group of LEDs, such that it will alternately produced equal duration pulses of diffuse illumination centered at selected wavelengths .The switching time of each LED sting is decided in accordance with camera frame rate. As matlab program runs, the camera received a trigger signal, captures video and began integration of frames. Multichromophore estimation can be done in MATLAB by using modified lamberts law and Linear mixing model. Table 6.1 Optical properties of tissue at specific wavelengths

Wavelength Optical properties

680nm Peak absorption of melanin

780nm Very deep penetration in tissue

800nm Very deep penetration in tissue, almost equal absorption for deoxyhemoglobin and oxyhemoglobin

System model

Fig 6.1.3 Proposed system



The set up for the proposed system is shown above. Source and detector are arranged in the same plane such that it can be used as a single module. Linear polarizer placed in front of LED ring will polarize the incident light to linearly polarized. The light incident on the sample will change its polarization if it undergo diffused reflection and reaches the CMOS camera module operated in video mode through cross polarizer. Image acquisition and further processing is controlled by MATLAB software Image Acquisition and Processing Image acquisition and processing of image is done by using MATLAB in PC or laptop. In matlab the video sequence acquired through USB camera module and converts it into frames. Then required images are saved as per the above design. i.e., save frames in each 100 ms for further processing. Image processing is required for the estimation of Hb, HbO2 and oxygen saturation under a lesion area. The flowchart for video acquisition and frame capture is shown below.



Multichromophore estimation -Image processing The images obtained at 3 wavelengths 680nm, 780nm and 800nm are needed to process using linear mixing model in order to get 2D estimate of melanin , Hb , HbO2 and oxygen saturation in that lesion area. The 3 images of lesion at different wavelengths are to be normalized with skin images without lesion of corresponding wavelengths in order to get absorption due to additional chromophores in lesion area. The normalization can be done by making use of modified Beer-Lamberts law.

This normalized image at each wavelength shows the linear combination of absorption due to additional chromophores ,oxyhemoglobin, deoxyhaemoglobin and melanin present in that imaging lesion area. The greyscale image intensity thus proportional to unknown estimate of chromophores and absorption coefficient of each chromophore at each wavelength. To find the unknown estimates, we have to solve 3 linear equations by absorption coefficients and 3 images. To find an estimate of the total relative absorption for Hb and HbO2 across the 100 nm between 680 nm and 780 nm, we compute a linear approximation of the area underneath the absorption curve for each chromophore, modified by the amount of that chromophore as follows: Hb = 0.5([Hb]μ680Hb + [Hb]μ780Hb )(100 nm) HbO2 = 0.5([HbO2 ]μ680HbO2 + [HbO2 ]μ780HbO2 )(100 nm) The HbO2 estimate itself relates to how much spectral absorption is caused by HbO2, and therefore, is a measure of blood oxygen saturation (SO2). Total blood is simply HbO2 + Hb, therefore, our normalized ratiometric measurement of percent oxygen saturation in the blood is the following: R =HbO2/(HbO2 + Hb) All of the aforementioned equations are evaluated pixel by pixel, resulting in 2-D solutions for estimates of [HbO2 ], [Hb], and R.



Hence, the linear assumption is made on a pixel-by-pixel basis, which allows for inhomogeneously distributed chromophores in the x-y plane. Inhomogeneity can still be present along the z-axis, however at present we assume a homogenous mixture which is visible from images of the skin surface. Tissue analysis can be done by analyzing the 2D estimates by considering the fact that there will be increased blood flow to the lesion due to angiogenesis, if there is any malignancy . So there will be comparable estimate of oxyhemoglobin along with melanin estimate in that area. For benign lesion there will be no or poor estimate of extra blood chromophore when compared with melanin estimate Linear mixing model Since the major chromophores in the skin are melanin, HbO2, and Hb, the total absorption is a function of the unknown amount of melanin [Mel], oxyhemoglobin [HbO2], and deoxyhemoglobin [Hb]:

where μaMel ( λ), μaHbO2 ( λ), μaHb ( λ) are the wavelength-dependent absorption coefficients of melanin, HbO2 , and Hb, respectively. With three unknowns, three equations, or three images taken at different wavelengths ( ie, 680nm,780nm and 800nm) are needed to estimate the amounts of each chromophore . Given the absorption coefficients of melanin, HbO2, and Hb, the linear system defined by the three equations can be used to estimate [Mel], [HbO2], and [Hb] present under the area imaged by the pixel. The [HbO2] estimate itself relates to how much spectral absorption is caused by HbO2, and therefore, is a measure of blood oxygen content. Another method of visualization is to compute a ratiometric measurement defining the blood oxygen saturation, [SO2], relative to the total amount of blood. Beer’s Law As light propagates through a turbid medium such as the skin, the intensity of light decreases due to absorption and scattering interactions with the medium. The extent of these interactions are characterized by the absorption and scattering coefficients, μa and μs. At the simplest level, μa is related to a change in light intensity by the Beer–



Lambert law (or Beer’s law for short), which states that for light transmitted through a medium over a path length of l is, I/I0 =eμal where the incident light intensity is I0, and the transmitted light intensity is I. Because Beer’s law only takes into account attenuated effects of absorption, not scattering, many attempts have been made to introduce and quantify the scattering corrections needed for Beer’s law .Such corrections can be mathematically complex, through reliance on solutions to the radiative transfer equation or other modeling. For highly scattering media, such as biological tissue, the diffusion equation is often used as an approximation of the radiative transport equation, where the dominant term in its solution for a semi-infinite slab is approximated by

where ɸ(l) is the fluence rate, C is a constant and

Consequently, an approximate scattering-corrected equation to Beer’s Law may be written as: I/I0 =eμeff l

The light intensity I is the intensity value which is directly read by a CCD detector, and can thus be likened to the image pixel value at each pixel location. Similarly, the incident light intensity I0 may be represented by the intensity of the same pixel in an image of skin background. In practice, a background image would be acquired on the patient on a nearby patch of skin, right next to the lesion. By normalizing with respect to this background, melanin absorption due to the patient’s skin colour is effectively cancelled out which eliminates this potential variable from analysis. Thus in the normalized image defined by I/I0, any additional melanin or blood due to the lesion itself will be visible as dark regions (I/I0<1), whereas the skin surrounding the lesion will have I=I0 approximately equal to 1. In this simplified model, l represents the depth of a homogenous absorbing object adjacent to the skin surface. Furthermore, since the model is a pixelated image-based representation, the relevant equation variables I, I0,



μa, and l are all functions of pixel location (x; y), and can therefore vary heterogeneously in the x-y plane. The ―true‖ relationship between the background corrected image I/I0 and μa is defined as an unknown function f:

Fig 6.1.4Handheld imaging probe with polariser

Fig 6.1.5 Experimental set up for real time multispectral imaging



Experiment Results Some of the multispectral images of tissue and the estimation of total melanin,HbO2 , Hb and their oxygen saturation images are shown below. Multispectral images at 680nm,780nm and 800 nm wavelength of a comparatively fair skin tissue is shown below. The pixel intensity of melanin estimate obtained is less than hemoglobin indicates the presence of low melanin in fair skin. Here multichromophore separation is done without normalizing with background skin image which results in the total estimate of each chromphore in that area.

Fig 6.1.6 Multispectral images of fingers with fair skin at (a) 680 nm, (b) 780 nm,and (c) 800 nm. (d) surface reflectance image. (e) melanin estimate. (f) HbO2 estimate. (g) Hb estimate. (h) Ratiometric image R. Similarly the MSI images taken at these wavelengths on a palm area also proves the decrease in melanin on that area when compared to hemoglobin. Also the MSI images at the wrist portion,there is slight increase in pixel intensity at the vein areas of deoxyhemoglobin estimate.



Fig 6.1.7: Multispectral images of palm at (a) 680 nm, (b) 780 nm,and (c) 800 nm. (d) surface reflectance image. (e) melanin estimate. (f) HbO2 estimate. (g) Hb estimate. (h) Ratiometric image R.

Fig 6.1.8 Multispectral images of wrist portion at (a) 680 nm, (b) 780 nm,and (c) 800 nm. (d) surface reflectance image. (e) melanin estimate. (f) HbO2 estimate. (g) Hb estimate. (h) Ratiometric image R.



Chapter 7

Conclusion and Future Scope The need of multispectral imaging source was studied and two source models for different applications were suggested and simulated in Zemax. The radiant intensity from the three types of leds at wavelengths 680nm, 780nm and 800nm were evaluated in rings of different diameter ranging from 1cm to 5cm. The number of leds in each ring was also varied and the resultant radiation pattern was evaluated.

From the results, it can be concluded that there is a tradeoff between the no of leds used and the viewing angle of the light emitting diode. For leds with smaller viewing angle more number of leds are to used for uniform illumination of an area.

Further, as the viewing angle of the led decreases, the application is limited to smaller areas and another limitation is posed by the size of the camera.

A sequentially switching multispectral imaging source comprising the three wavelengths was developed.

This study can be extended to evaluate the source characteristics with the use of polariser and cross polariser. Also the multispectral source can be extended to higher number of wavelengths for more spectral information.



References

1. Hyperspectral Imaging Spectroscopy A Look at Real-Life Applications John R. Gilchrist, Gilden Photonics Ltd.; Timo Hyvärinen, Spectral Imaging Ltd.

2. http://en.wikipedia.org/wiki/Multispectral_image

3. Multispectral Imaging for Your Application

By Steve Smith, PhD, Pixelteq, Golden, Colo

4. Adaptive Illumination Source for Multispectral Vision System Applied to Material Discrimination Olga M. Conde, Adolfo Cobo, Paulino Cantero, David Conde, Jesús Mirapeix, Ana M. Cubillas, José M. López-Higuera Photonics Engineering Group, Dep. TEISA, Universidad de Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain

5. How Are LED Illumination Based Multispectral Imaging Systems Influenced by

Different Factors? Image and Signal Processing Lecture Notes in Computer Science Volume 8509, 2014, pp 61-71 Raju Shrestha, Jon Yngve Hardeberg

6. Evaluation and comparison of multispectral imaging systems Raju Shrestha and Jon Yngve Hardeberg The Norwegian Colour and Visual Computing Laboratory, Gjøvik University College, Norway

7. Multispectral imaging using LED illumination and an RGB camera Raju Shrestha and Jon Yngve Hardeberg The Norwegian Colour and Visual Computing Laboratory, Gjøvik University College, Norway

8. Multispectral Filter Arrays: Recent Advances and Practical Implementation

Pierre-Jean Lapray, XingboWang, Jean-Baptiste Thomas and Pierre Gouton

http://en.wikipedia.org/wiki/Multispectral_image

http://link.springer.com/book/10.1007/978-3-319-07998-1

http://link.springer.com/bookseries/558

http://link.springer.com/search?facet-author=%22Raju+Shrestha%22

http://link.springer.com/search?facet-author=%22Jon+Yngve+Hardeberg%22



9. http://www.thorlabs.com/ 10. http://biomedicaloptics.spiedigitallibrary.org/article.aspx?articleid=1816617

11. Multispectral Transillumination Imaging of Skin Lesions for Oxygenated and Deoxygenated Hemoglobin Measurement and Hb distribution in the skin, as well as a ratiometric measurement descriptive of the relative oxygen saturation level of the blood volume. Brian D’Alessandro, Atam P. Dhawan

12. "Multispectral Optical Imaging of Skin-Lesions for Detection of Malignant

Melanomas," A. P. Dhawan, B. D'Alessandro, S. Patwardhan, and N. Mullani,

13. https://www.zemax.com/

http://www.thorlabs.com/

http://biomedicaloptics.spiedigitallibrary.org/article.aspx?articleid=1816617

design and evaluation of led illumination source for...

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