Medical Imaging

Mohammad Dawood

Department of Computer Science

University of MünsterGermany

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Medical Imaging, SS-2010

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What is medical imaging?

Medical imaging is the process of acquiring images without or with minimal invasion for the purpose of detecting, diagnosing, quantifying or treating a disease.

Techniques and methods from image processing are used to assist the clinicians.

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Medical Imaging, SS-2010

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Structure of the Course

1. Basics of Image processing2. Medical Image modalities3. Reconstruction4. Registration5. Segmentation6. Enhancement

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Medical Imaging, SS-2010

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Image processing

Signal processing with an image as an input and an image or a set of features as output.

Definitions

ImageDomain

In the discrete case

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Medical Imaging, SS-2010

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Classical methods of image processing include

Grayscale transformationsColor spacesFilteringEdge detectionMorphological operations

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Medical Imaging, SS-2010

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Grayscale transformations

The human eye can distinguish between different colors with estimates ranging from 100,000 to 10 million!

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Medical Imaging, SS-2010

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Michelson contrast :

Weber contrast:

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Medical Imaging, SS-2010

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Grayscale Transforms

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Medical Imaging, SS-2010

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Grayscale transformations

Three of the most common grayscale transforms are:

1. Linear2. Logarithmic3. Power law

Point operations

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Medical Imaging, SS-2010

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Linear color domain transform

X-Ray Mammogram

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Medical Imaging, SS-2010

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Power law

MRI of Spinal cord

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Medical Imaging, SS-2010

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Power law

CT of Head

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Medical Imaging, SS-2010

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Histogram

Histogram function :

Probability function:

Cumulative histogram:

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Medical Imaging, SS-2010

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Histogram Equalization

MRI of Spinal cord

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Medical Imaging, SS-2010

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Histogram equalization

Mammograms

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Medical Imaging, SS-2010

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Adaptive/Local Histogram Equalization

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Medical Imaging, SS-2010

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Local Histogram Equalization

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Medical Imaging, SS-2010

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Use of color spaces

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Medical Imaging, SS-2010

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Use of different color spaces

The continuous spectrum visible to human eyes

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Medical Imaging, SS-2010

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Use of different color spaces

RGB (Red, Green, Blue)

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Medical Imaging, SS-2010

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Use of different color spaces

RGB (Red Green Blue)Cardiac PET

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Use of different color spaces

HSV (Hue, Saturation, Value)

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Medical Imaging, SS-2010

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Use of different color spaces

HSV (Hue, Saturation, Value)

S=1, V=1

V=1

S=1

Cardiac PET

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Medical Imaging, SS-2010

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Using different spectrums

Cardiac PET

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Fourier Transform

Euler’s formula:

Fourier transform:

Inverse Fourier transform:

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Fourier Transform

Respiratory signal

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Fourier Transform

Convolution theorm

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Spatial filtering

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Spatial connectivity

2D- 4 connectivity- 8 connectivity

3D

- 6 connectivity- 18 connectivity- 26 connectivity

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Spatial filtering (local operators)

Filters are used in image processing for various purposes e.g. noise reduction, edge detection, pattern recognition.

1 1 1

1 1 1

1 1 1

0 7 3 -2 3

-1 8 3 5 -6

4 0 3 7 4

0 1 -5 0 -3

7 1 4 6 -8

f h f*

(0*1+7*1+3*1-1*1+8*1+3*1+4*1+0*1+3)*1/9 = 3

0 7 3 -2 3

-1 3 3 5 -6

4 0 3 7 4

0 1 -5 0 -3

7 1 4 6 -8

* 1/9

Applied only to red cell

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Noise reductionAveraging filter

* *1/9 =

3 3 3 3 0

3 5 3 3 0

3 3 3 3 0

0 0 0 0 0

0 0 0 0 0

1 1 1

1 1 1

1 1 1

3 3 3 3 0

3 3.2 3 3 0

3 3 3 3 0

0 1 1 0.7 0

0 0 0 0 0

Cardiac PET, averaging with 5x5

Applied only to red cells

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Median filter

Median = Middle value of the set

Example

- given S = {1, 5, 2, 0, -3, 8, 0}- sort S = {-3, 0, 0, 1, 2, 5, 8}

median(S)= 1

What happens if |s| is even?- given S = {1, 5, 2, 0, -3, 8, 0, -5}- sort S = {-3, -5, 0, 0, 1, 2, 5, 8}

median(S)= 0.5

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Medical Imaging, SS-2010

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Noise reductionMedian filter

* median filter =

3 3 3 3 0

3 5 3 3 0

3 3 3 3 0

0 0 0 0 0

0 0 0 0 0

3 3 3 3 0

3 3 3 3 0

3 3 3 3 0

0 0 0 0 0

0 0 0 0 0Applied only to red cells

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Medical Imaging, SS-2010

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Noise reductionGaussian filter

Gauss function is defined as:

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Medical Imaging, SS-2010

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Original Averaging (5x5) Median(5x5) Gaussian (5x5)

Noise reductionComparison