Medical Image Analysis Dr. Mohammad Dawood Department of Computer Science University of Münster Germany

2 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood 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.

3 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Structure of the Course 1. Basics of Image processing 2. Medical Image modalities 3. Reconstruction 4. Registration 5. Segmentation 6. Enhancement

4 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Image processing Signal processing with an image as an input and an image or a set of features as output. Definitions Image Domain In the discrete case

5 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Classical methods of image processing include Grayscale transformations Color spaces Filtering Edge detection Morphological operations

6 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Grayscale transformations The human eye can distinguish between different colors with estimates ranging from 100,000 to 10 million!

7 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Michelson contrast : Weber contrast:

8 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Grayscale Transforms

9 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Grayscale transformations Three of the most common grayscale transforms are: 1.Linear 2.Logarithmic 3.Power law Point operations

10 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Linear color domain transform X-Ray Mammogram

11 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Power law MRI of Spinal cord

12 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Power law CT of Head

13 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Histogram Histogram function: Probability function: Cumulative histogram:

14 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Histogram Equalization MRI of Spinal cord

15 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Histogram equalization Mammograms

16 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Adaptive/Local Histogram Equalization

17 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Local Histogram Equalization

18 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Use of color spaces

19 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Use of different color spaces The continuous spectrum visible to human eyes

20 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Use of different color spaces RGB (Red, Green, Blue)

21 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Use of different color spaces RGB (Red Green Blue) Cardiac PET

22 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Use of different color spaces HSV (Hue, Saturation, Value)

23 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Use of different color spaces HSV (Hue, Saturation, Value) S=1, V=1 V=1 S=1 Cardiac PET

24 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Using different spectrums Cardiac PET

25 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Fourier Transform Euler’s formula: Fourier transform: Inverse Fourier transform:

26 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Fourier Transform Respiratory signal

27 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood

28 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Fourier Transform Convolution theorm

29 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Spatial filtering

30 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Spatial connectivity 2D - 4 connectivity - 8 connectivity 3D - 6 connectivity - 18 connectivity - 26 connectivity

31 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Spatial filtering (local operators) Filters are used in image processing for various purposes e.g. noise reduction, edge detection, pattern recognition f h f* (0*1+7*1+3*1-1*1+8*1+3*1+4*1+0*1+3)*1/9 = * 1/9 Applied only to red cell

32 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Noise reduction Averaging filter * *1/9= Cardiac PET, averaging with 5x5 Applied only to red cells

33 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Median filter Median = Middle value of the set Example - givenS = {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? - givenS = {1, 5, 2, 0, -3, 8, 0, -5} - sort S = {-3, -5, 0, 0, 1, 2, 5, 8} median(S)= 0.5

34 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Noise reduction Median filter * median filter = Applied only to red cells

35 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood Noise reduction Gaussian filter Gauss function is defined as:

36 Medical Image Analysis, SS-2015 Dr. Mohammad Dawood OriginalAveraging (5x5) Median(5x5) Gaussian (5x5) Noise reduction Comparison