imaging science to ubiquitous imaging

7/29/2019 Imaging Science to Ubiquitous Imaging

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Imaging Science to Ubiquitous Imaging

An image of a bird sometime in the future may be more than just a picture. It

could also carry date and models that can produce the birds song, display its

feeding or mating behavior, and describe its habitat and food preferences-

Excerpts from the keynote address titled The Ubiquitous imaging, delivered at the

International Congress of Imaging Science, 2006.

Images have become ubiquitous part in our everyday life and culture. The

application of images have become very much essential for the advancement of

different fields ranging from science and technology, to biomedical research

and clinical practice, to commerce and industry, to entertainment andadvertising, and to communication and education and so forth. Imaging science

is an interdisciplinary field which requires knowledge of the disciplines of

physics, engineering, chemistry, biology, and medicine. The rapid advances in

imaging technology and techniques have led to the ever increasing growth of

this field of study. It mainly deals with the detection, spatiotemporal

localization, recording, display, visual observation, and measurement of object

properties from images that are obtained from various imaging modalities.

Properties of various objects of interests are studied by means of computer-

based imaging systems that provide information needed for an understanding oftheir structure and functions. The main goal of imaging science is to develop an

imaging system to yield images that are:

1. more accurate representations of objects, by reducing blurring,

distortions, and other artifacts.

2. more reproducible, by reducing random fluctuations or noise without

increasing observation time.

3. complete, by producing three-dimensional images of three-dimensional

objects, and by increasing the number of object properties that can be

imaged.

4. intelligent, by rendering and displaying them in a manner that makes the

extraction, interpretation, and assimilation of information more accurate

and reproducible.

Subfields within imaging science include: image processing, 3D computer

graphics, animations, atmospheric optics, astronomical imaging, digital

imaging, color science, digital photography, holography, magnetic resonance

imaging, optics, remote sensing, radar imaging, radiometry, ultrasound imaging,

thermal imaging, visual perception, and various printing technologies.

Dr. Bhabesh Deka

Associate Professor

Dept. of ECE

Tezpur University


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Various issues involved in imaging science1. Image-data acquisition

Detection and angular/spatial/temporal localization of radiation or other

property associated with the objects. It reflects in the design of the front-

end hardware of any imaging device (e.g., the optical components of amicroscope) which, in turn, determines the sensitivity, resolution, and

other quality characteristics of the imaging system.

2. Image recovery and enhancement

It involves the mathematical concepts and algorithms for producing an

image from the acquired data (e.g., image reconstruction from

projections, e.g. MRI, CT, etc.). Image enhancement aims to improve the

quality (e.g., to sharpen edges and to reduce distortion, interference,

artifacts, and image noise) of an image by using linear and non-linear

image processing algorithms.3. Image recording and distribution

It involves the mathematical algorithms for image compression, storage,

retrieval and transmission.

4. Image display/visualization

Monochrome/colour; 2-D and 3-D display of images of real objects or of

hypothetical objects and processes obtained from computer simulations

based on mathematical models.

5. Image observation and observer performance

It involves the response characteristics of the eye-brain system and

measures the ability of the observer to perform visual tasks, as well as

strategies for the development of interactive, analytic, diagnostic, and

adaptive software to facilitate observer performance, based on knowledge

of human vision.

6. Image analysis

Segmentation and measurement; morphological analysis; pattern

recognition; feature extraction; artificial intelligence

7. Image evaluation

Evaluation involves measures of image quality (maximum signal-to-

noise-ratio, information content). Subjective evaluation of images byconsidering viewer performance. Social, cultural, aesthetic criteria, etc.

Various Imaging modalitiesThere are many techniques through which an image can be generated.

These are governed by different physical principles and sources of energy

depending on the target applications ranging from simple photography to

imaging of the living cells in a biological system. A brief classification of

various imaging modalities can be made as follows.

a) Microscopy- Light; UV; X-Ray; fluorescence; electron- & ion-beam, etc.


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b) X-Ray imaging

-Direct, screen-film, CT, etc.

c) Magnetic resonance imaging

-Conventional and spectroscopic

d) Radionuclide imaging-Positron Emission Tomography (PET), Single photon emission

computerized (SPECT) tomography

e) Ultrasound

-Material testing, biomedical

f) Holography & pseudo-holography

-Light, X-ray, gamma ray

g) Telescopic imaging

-X-ray, UV, Optical, Infra red, radio

h) Computer generated-Image simulation/animation from mathematical models

i) Graphic arts & sciences

Drawing, etching, painting, photography/video, printing, television

j) Computer vision

-CCD arrays/robotics

State-of-the-art applications of imaging scienceCurrently, imaging scientists around the globe are striving for the

development of imaging systems that could interpret images acquired from

different modalities: viz. images of natural scenes acquired by a camera, CT

scans and other data obtained with biomedical imaging devices and aerial and

satellite images acquired by remote sensing, etc. without any human

intervention.

Significant developments in the imaging technology have led to the

development of high-tech cameras and other imaging devices. Also, a human

observer can effortlessly make out the semantic understanding of various

objects appearing in images, but, a machine cannot interpret the same via

programming with meaning full accuracy. Therefore, it remains a major open

challenge for the imaging scientists working in the related fields, including

the automated medical diagnosis, the industrial automation, and the securityand surveillance to bridge this semantic gap.

A few state-of-the-art applications of the imaging science are briefly

mentioned below.

1. Medical Image Analysis:

Combining biomedical imaging science with

computational modeling, it is possible to infer,

noninvasively, the structural and functional

properties of complex biological systems. For

example, with the help of MRI it is possible todetect noninvasively the presence of tumors


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and/or follow up the treatment after detection or studying the cardiac

activity from a sequence of cardiac echo images.

2. Computer Vision:

Humans are naturally able to recognize the objects

in a scene much better than computers. We caneffortlessly distinguish among a remarkable

variety of objects, actions and interactions even in

complex, cluttered scenes, e.g., recognizing a

particular face in a crowd. In contrast,

automatically interpreting such scenes using a

computer is very difficult. The challenge here is to

develop fully automatic computer vision algorithms for the interpretation

of complex scenes. Examples: segmenting and tracking of moving

objects in a video, recognizing dynamic textures (water, smoke, fire) in avideo, recognizing human activities in a video, and modeling and

recognizing skill in surgical motion and video data.

3. Computational Biology:

A formidable challenge is the dissection of gene

regulatory networks to show how eukaryotic cells

coordinate and govern patterns of gene expression.

Here the imaging scientist might develop graphical

models to capture the statistical dependency

structure among gene and protein expression

values.

The future of imaging science is very bright and challenging. We are already

into the era of ubiquitous imaging, where imaging is everywhere yet

unobtrusive. This is paralleled to the concept of ubiquitous computing where

everything is intelligent and connected. There is a plenty of research scopes,

in various domains including the machine vision, the biomedical imaging

and analysis, and the digital imaging. Problems in machine vision, such as

the automatic interpretation of shapes and other objects appearing in images,MR perfusion for assessing early solid tumor response to therapy, Diffusion

Tensor Imaging with MR to delineate important nerve tracts in relation to

brain tumors, etc. are some of the ongoing active research problems.

[ Some of the contents of this article are written based on the paper that was presented

at a colloquium entitled "Images of Science: Science of Images," organized by Albert V.

Crewe, held January 13 and 14, 1992, at the National Academy of Sciences, Washington,

DC. ]

imaging science to ubiquitous imaging

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