3D Digital Cleansing Using Segmentation Rays

1. 3D Digital CleansingUsing Segmentation Rays Authors: Sarang Lakare, Ming Wan, Mie Sato and Arie Kaufman Source: In Proceedings of the IEEE Visualization Conference, pp.37–44, 2000 Speaker: Wen-Ping Chuang Adviser: Ku-Yaw Chang

2. Outline • Introduction • Segmentation approach • Result • Conclusion

3. Introduction(1/6) • Virtual screening techniques • Volume rendering techniques have grown rapidly • Interactive frame rates generate accurate results • Organs have complex structures • Segmentation plays a very important role

4. Introduction(2/6) • Segmentation • Simple threshold • Get complicated due to partial volume effect • Cause unwanted and non-existing surfaces • Combine the threshold and flood-fill techniques • Flexible • Segmentation rays • Volumetric contrast enhancement

5. Introduction(3/6) • Polyps • Potentially cancerous • More than 5 mm • Consider potentially malignant • Need to be removed

6. Introduction(4/6) • Physical colon cleansing • Large amounts of liquids • Medications • Enemas

7. Introduction(5/6) • A friendly virtual colonoscopy system • Bypass the colon physical cleansing • Need for segmenting the residual material • Give a clean colon to the rendering algorithm

8. Introduction(6/6) • A new bowel preparation scheme • Enhance the stool and fluid densities • Take and reconstruct into a 3D dataset • Partial volume effect • Have not a clear boundary • Worsen situation • Finite resolution • Low contrast

9. Outline • Introduction • Segmentation approach • Result • Conclusion

10. Segmentation approach • Threshold • Morphological operations • Proposed approach

11. Threshold • Human abdomen • High density materials • Bone • Fluid • Stool • Soft tissue • Air

12. Threshold • Disadvantages • Not remove PVE voxels • Sensitive for each range of intensities • Gives rise to aliasing effects at the inner colon boundary Fig.1 Fig.2 Fig.3

13. Segmentation approach • Threshold • Morphological operations • Proposed approach

14. Morphological operations • Succession operation • Such as dilation and erosion • Flood-fill on all the fluid and stool regions • A sequence of dilates and erodes to remove the PVE voxels

15. Morphological operations • Dilation • The dilation of A by B • B is the structuring element

16. Morphological operations • Erosion • The Erosion of A by B • B is the structuring element

17. Morphological operations • Highly twistedaffect the inner contour of the colon • Dilate followed by erode • Can fill in holes • Erode followed by dilate • Can remove noise + +

18. Morphological operations • Disadvantages • Task considering the large number of such regions • Require a lot of human intervention • Slow down the entire process of segmentation • Result in some fluid/stool regions being ignored

19. Segmentation approach • Threshold • Morphological operations • Proposed approach

20. Proposed approach • Approximate intensity based classification • Classify the intensity values in the histogram • Depend on the number and type of district regions • Region boundaries • Define by approximate thresholds • Flexible • Unique intensity profiles at different intersections • Study and store

21. Proposed approach • Approximate intensity based classification

22. Proposed approach • Region growing • Detect and mark the interior AIR region • A smooth horizontal surface due to gravity • Take a seed point to mark all the air voxels • Reach no longer belong the air voxels

23. Proposed approach • Selecting starting points for segmentation rays • Critical to the overall speed of the algorithm • Select fewer the voxels get faster the algorithm • Assign the boundary voxels are simplest and fastest

24. Proposed approach • Detecting intersections using segmentation rays • Critical to the detection of the polyps • Remove most of the PVE voxels • Give an improved colon contour

25. Proposed approach • Segmentation rays • From each of the AIR boundary voxel • 26-connected-neighbor directions • Stop and ignore • Not find any intersection after traversing a certain distance +

26. Proposed approach • Volumetric contrast enhancement • A programmed transfer function • Unwanted materials are removed • Similar to contrast enhancement • A smooth transfer function • Get no-aliasing boundaries • Improve the quality of volume rendering

27. Outline • Introduction • Segmentation approach • Result • Conclusion

28. Result • Virtual colonoscopy system • Automatic • Histogram classification • Seed point detection • A fully automatic solution • Segmentation • Digital colon cleansing

29. Result • Crux of this paper algorithm • Characterizing the intersections • Accurate a result as a manual segmentation • Not miss even a single intersection

30. Result • A cross-section of the CT data showing colon (L) (R)

31. Result • Volume rendered images showing (L) (R)

32. Outline • Introduction • Segmentation approach • Result • Conclusion

33. Conclusion • Advantages • Fast and accurate segmentation algorithm • Remove the partial volume effect • General algorithm • Use by any application similar to virtual colonoscopy

34. Conclusion • Future work • Build an interactive segmentation system • Pick intersection characteristics using a mouse • Find a particular intersection assigning classification/reconstruction tasks to the rays • Add visual feedback • Render and display the segmented dataset

35. Thank you for listening THE END