1. MedIX – Summer 07 Lucia Dettori (room 745) ldettori@cti.depaul.edu

2. Projects Contrast Enhancement  A brief summary of what has been done  Things I would like to explore next Texture classification Evaluations of segmentation algorithms

3. Broad goal – The big picture Manipulate (medical) images to facilitate the radiologists’ job of recognizing features and pathologies in radiological images Improve the visual quality of an image and automatically “highlight” certain features Give them a way to focus on subsets of the image that are of interest to them

4. Contrast is all we “see” Human eye identifies details by contrasting an object (foreground) and its background Improve the quality of the image by creating (color) contrast In our case we are talking about CT scan images with different levels of grey

5. Contrast enhancement Take the gray level intensities of an image and proportionally redistribute them Some mapping is necessary anyway since the images are based on 12 bits of information (gray levels ranges from 0 to 4095) and on these monitors we can only display 8 bits (gray levels from 0 to 255) Can we do a better job?

6. Example

7. Techniques implemented Linear binning  Linearly redistribute the intensities from a range of 0 - 4095 to a range of 0-255 over a chosen number of bins Nonlinear binning  First identify clusters of intensities then use those to guide the redistribution of gray levels Histogram equalization

8. Soft tissues or bones? Radiologist might be interested in only some parts of the image:  Soft tissues  Lungs  Bones Each of these correspond to a different range of grey levels

9. Local contrast enhancement Instead of trying to enhance the entire picture, concentrate the enhancing power in the range of intensities you are interested in Window enhancement Multiple windows enhancement

10. Where are we now? Previous students have created an application (C#) that allows the user to select windows and technique and display the enhanced image

11. Things I’d like to explore Find an objective way to measure the improvement resulting from the contrast enhancement See how these techniques perform when using different image modalities beyond CT-scans Explore additional contrast enhancement techniques

12. Projects Contrast Enhancement Texture classification  A brief summary of what has been done  Things I would like to explore next Evaluations of segmentation algorithms

13. The big pictures Given a pre-segmented organ region, can you tell me what it is: kidney, heart etc? It depends … on its texture Identify image features that give texture information Find rules that distinguish the texture features of one organ from another

14. Texture Classification Process at a glance Apply filter To the image Physician annotated Organ/Tissue Texture Descriptors Classifier (Decision Tree) Classification rules for tissue/organs in CT images LiverKidneyBoneSpleenHeart

15. Image Data Set OrganQtyImageOrganQtyImage Kidney223 Aorta66 Liver260 IP Fat59 Spleen95 Muscle198 Trabecular Bone39 SQ Fat157 Lung15 Total Images 1112

16. Step3 – Texture features extraction Apply Gabor filters to the image Texture Descriptors For example: Mean, standard deviation, energy, entropy etc.. Array of texture descriptors [T1, T2, T3, …, Tn] Physician annotated Organ/Tissue Liver

17. Step4 - Classification Apply Gabor filters to the image Texture Descriptors Classification rules for tissue/organs in CT images The process of identifying a region as part of a class (organ) based on its texture properties. Decision tree Predicts the organ from the values of the texture descriptors Training / Testing Classification performance measures Physician annotated Organ/Tissue

18. Step5 – Evaluating the classifier

19. Apply Gabor filters to the image Texture Descriptors Classification rules for tissue/organs in CT images Decision tree Things I would like to explore Wedgelet transfors Fractal Dimensions Performance measures Test Gabor texture descriptors on additional images and natural images Physician annotated Organ/Tissue

20. Projects Contrast enhancement Texture classification Evaluations of segmentation algorithms  Brief summary of what has been done  Things I would like to explore next

21. Texture segmentation Given an image, can you tell me how many organs you have? That was easy enough. Can you tell which organs they are?  Identifying regions with similar texture  Identifying which texture it is to label the organ

22. A couple of key questions Can you do it better by varying a parameter? How do you choose the values of your segmentation parameters? If it looks better is it really better?

23. A couple of key questions Parameter optimization Performance evaluation

24. 1 3 4 2 0.87 0.56 0.50 0.75

25. Increasing value of a segmentation parameter Ground Truth Regions key Machine Segmentations How do I decide what the optimal value of the parameter is? How good a segmentation is it?

26. The “goodness” metric A single value that assigns a rating to a particular segmentation based on how well the machine segmented regions “match” the regions in the ground truth images

27. Region Categories Ground Truth vs. Machine Segmented Correctly Detected Over Segmented Under Segmented Missed Noise GT MS

28. CORRECTLY DETECTED OVER SEGMENTED UNDER SEGMENTED A Missed region is a GT region that does not participate in any instance of CD, OS, or US A Noise region is an MS region that does not participate in any instance of CD, OS, or US Index for each region

29. The “Goodness” Metric good = Correct Detection Index bad = 1-Correct Detection Index goodness = good-bad*weight 1.0 Ceiling = CDind Floor = 2*CDind-1 Weight Range = CDind-1

30. How can we use the metric? Create a set of ground truth mosaic using radiologist-labels images of pure patches of organ tissues Apply segmentation algorithm Optimize the segmentation parameters using the metric Apply optimized algorithm to the “real” image

31. Ground Truth Region key T=1000; GM= -.94 T=4000; GM=.74 T=3000; GM=.73 T=2000; GM= -.02 T=5000; GM=.75T=6000; GM=.08

32. Done so far Used the metric on a block-wise walevet- based segmentation algorithm on some sample mosaic

33. To be done Fully test the metric on a wide range of segmentation algorithms Decouple the various components of the metric and test the individual performance measures instead of the overall score Extend the metric to measure one region vs background segmentation