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

  2. 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. Structure of the Course • Basics of Image processing • Medical Image modalities • Reconstruction • Registration • Segmentation • Enhancement

  4. 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. Classical methods of image processing include • Grayscale transformations • Color spaces • Filtering • Edge detection • Morphological operations

  6. Grayscale transformations • The human eye can distinguish between different colors with estimates ranging from 100,000 to 10 million!

  7. Michelson contrast : Weber contrast:

  8. Grayscale Transforms

  9. Grayscale transformations Three of the most common grayscale transforms are: Linear Logarithmic Power law Point operations

  10. Linear color domain transform X-Ray Mammogram

  11. Power law MRI of Spinal cord

  12. Power law CT of Head

  13. Histogram Histogram function : Probability function: Cumulative histogram:

  14. Histogram Equalization MRI of Spinal cord

  15. Histogram equalization Mammograms

  16. Adaptive/Local Histogram Equalization

  17. Local Histogram Equalization

  18. Use of color spaces

  19. Use of different color spaces The continuous spectrum visible to human eyes

  20. Use of different color spaces RGB (Red, Green, Blue)

  21. Use of different color spaces RGB (Red Green Blue) Cardiac PET

  22. Use of different color spaces HSV (Hue, Saturation, Value)

  23. Use of different color spaces HSV (Hue, Saturation, Value) S=1, V=1 V=1 S=1 Cardiac PET

  24. Using different spectrums Cardiac PET

  25. Fourier Transform Euler’s formula: Fourier transform: Inverse Fourier transform:

  26. Fourier Transform Respiratory signal

  27. Fourier Transform Convolution theorm

  28. Spatial filtering

  29. Spatial connectivity 2D - 4 connectivity - 8 connectivity 3D - 6 connectivity - 18 connectivity - 26 connectivity

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

  31. Noise reduction Averaging filter * *1/9 = Applied only to red cells Cardiac PET, averaging with 5x5

  32. 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

  33. Noise reduction Median filter * median filter = Applied only to red cells

  34. Noise reduction Gaussian filter Gauss function is defined as:

  35. Noise reduction Comparison Original Averaging (5x5) Median(5x5) Gaussian (5x5)