1. Medical Imaging Mohammad Dawood Department of Computer Science University of Münster Germany

2. 2 Medical Imaging, SS-2010 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. 3 Medical Imaging, SS-2010 Mohammad Dawood Structure of the Course 1. Basics of Image processing 2. Medical Image modalities 3. Reconstruction 4. Registration 5. Segmentation 6. Enhancement

4. 4 Medical Imaging, SS-2010 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. 5 Medical Imaging, SS-2010 Mohammad Dawood Classical methods of image processing include Grayscale transformations Color spaces Filtering Edge detection Morphological operations

6. 6 Medical Imaging, SS-2010 Mohammad Dawood Grayscale transformations The human eye can distinguish between different colors with estimates ranging from 100,000 to 10 million!

7. 7 Medical Imaging, SS-2010 Mohammad Dawood Michelson contrast : Weber contrast:

8. 8 Medical Imaging, SS-2010 Mohammad Dawood Grayscale Transforms

9. 9 Medical Imaging, SS-2010 Mohammad Dawood Grayscale transformations Three of the most common grayscale transforms are: 1.Linear 2.Logarithmic 3.Power law Point operations

10. 10 Medical Imaging, SS-2010 Mohammad Dawood Linear color domain transform X-Ray Mammogram

11. 11 Medical Imaging, SS-2010 Mohammad Dawood Power law MRI of Spinal cord

12. 12 Medical Imaging, SS-2010 Mohammad Dawood Power law CT of Head

13. 13 Medical Imaging, SS-2010 Mohammad Dawood Histogram Histogram function: Probability function: Cumulative histogram:

14. 14 Medical Imaging, SS-2010 Mohammad Dawood Histogram Equalization MRI of Spinal cord

15. 15 Medical Imaging, SS-2010 Mohammad Dawood Histogram equalization Mammograms

16. 16 Medical Imaging, SS-2010 Mohammad Dawood Adaptive/Local Histogram Equalization

17. 17 Medical Imaging, SS-2010 Mohammad Dawood Local Histogram Equalization

18. 18 Medical Imaging, SS-2010 Mohammad Dawood Use of color spaces

19. 19 Medical Imaging, SS-2010 Mohammad Dawood Use of different color spaces The continuous spectrum visible to human eyes

20. 20 Medical Imaging, SS-2010 Mohammad Dawood Use of different color spaces RGB (Red, Green, Blue)

21. 21 Medical Imaging, SS-2010 Mohammad Dawood Use of different color spaces RGB (Red Green Blue) Cardiac PET

22. 22 Medical Imaging, SS-2010 Mohammad Dawood Use of different color spaces HSV (Hue, Saturation, Value)

23. 23 Medical Imaging, SS-2010 Mohammad Dawood Use of different color spaces HSV (Hue, Saturation, Value) S=1, V=1 V=1 S=1 Cardiac PET

24. 24 Medical Imaging, SS-2010 Mohammad Dawood Using different spectrums Cardiac PET

25. 25 Medical Imaging, SS-2010 Mohammad Dawood Fourier Transform Euler’s formula: Fourier transform: Inverse Fourier transform:

26. 26 Medical Imaging, SS-2010 Mohammad Dawood Fourier Transform Respiratory signal

27. 27 Medical Imaging, SS-2010 Mohammad Dawood

28. 28 Medical Imaging, SS-2010 Mohammad Dawood Fourier Transform Convolution theorm

29. 29 Medical Imaging, SS-2010 Mohammad Dawood Spatial filtering

30. 30 Medical Imaging, SS-2010 Mohammad Dawood Spatial connectivity 2D - 4 connectivity - 8 connectivity 3D - 6 connectivity - 18 connectivity - 26 connectivity

31. 31 Medical Imaging, SS-2010 Mohammad Dawood Spatial filtering (local operators) Filters are used in image processing for various purposes e.g. noise reduction, edge detection, pattern recognition. 111 111 111 073-23 835-6 40374 01-50-3 7146-8 f h f* (0*1+7*1+3*1-1*1+8*1+3*1+4*1+0*1+3)*1/9 = 3 073-23 335-6 40374 01-50-3 7146-8 * 1/9 Applied only to red cell

32. 32 Medical Imaging, SS-2010 Mohammad Dawood Noise reduction Averaging filter * *1/9= 33330 35330 33330 00000 00000 111 111 111 33330 33.2330 33330 0110.70 00000 Cardiac PET, averaging with 5x5 Applied only to red cells

33. 33 Medical Imaging, SS-2010 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. 34 Medical Imaging, SS-2010 Mohammad Dawood Noise reduction Median filter * median filter = 33330 35330 33330 00000 00000 33330 33330 33330 00000 00000 Applied only to red cells

35. 35 Medical Imaging, SS-2010 Mohammad Dawood Noise reduction Gaussian filter Gauss function is defined as:

36. 36 Medical Imaging, SS-2010 Mohammad Dawood OriginalAveraging (5x5) Median(5x5) Gaussian (5x5) Noise reduction Comparison