SlideServe
Télécharger Obtenir Pro
Menu
Accueil Pro
Préférences
ApparenceLight Aide et commentaires
© SlideServe · v2026

Medical Imaging

dacia avatar
dacia
0 diapositives · 358 Views · Jul 08, 2014

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

#medical imaging #medical imaging ppt #powerpoint #presentation
Medical Imaging - slide 1
01 / 1Medical Imaging
1 / 17

Télécharger la présentation

Medical Imaging
Une image / un lien ci-dessous est fourni (tel quel) pour télécharger la présentation

Politique de téléchargement : le contenu du site vous est fourni TEL QUEL pour votre information et votre usage personnel ; il ne peut être vendu, concédé sous licence ni partagé sur d’autres sites sans l’accord de son auteur.Le contenu vous est fourni TEL QUEL, pour votre information et votre usage personnel uniquement. Téléchargez la présentation en cliquant sur ce lien. Lors du téléchargement, si vous ne parvenez pas à télécharger une présentation, l’éditeur a peut-être supprimé le fichier de son serveur.Pendant le téléchargement, si vous ne parvenez pas à obtenir une présentation, le fichier a peut-être été supprimé par l’éditeur.

F I N

Plus de contenu associé

Transcription de la présentation
  1. Medical Imaging 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)