Outline

1. Surface Normal Overlap: A Computer-Aided Detection Algorithm With Application to Colonic Polyps and Lung Nodules in Helical CT Authors: David S. Paik*, Christopher F. Beaulieu, Geoffrey D. Rubin, BurakAcar, R. Brooke Jeffrey, Jr., Judy Yee,JoyoniDey, and Sandy Napel Source: IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 23, NO. 6, JUNE 2004 Speaker: Wen-Ping Chuang Adviser: Ku-Yaw Chang

2. Outline Introduction CAD algorithm Theoretical analysis Conclusion

3. Introduction • Lung cancer • Lung Nodules • Colon cancer • Colonic Polyps • Attention and eye fatigue • Accuracy and efficiency

4. Introduction • CAD methods • Computed tomography images • CT lung nodule detection • CT colonic polyp detection

5. Introduction

6. Introduction

7. Introduction • Surface normal overlap method • On 8 CT datasets

8. Outline Introduction CAD algorithm Theoretical analysis Conclusion

9. CAD algorithm Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection

10. Pre-Processing and Segmentation • CT volume data • I(x,y,z)：(0.6mm)3 • Reduce any bias • Lesions at different orientations • Datasets with different voxel sizes • Segmentation automatically • Colon lumen • Lung parenchyma

11. Pre-Processing and Segmentation • Segmentation automatically (S1) • All air intensity voxels • I(x,y,z) ＜-700HU • Negatively • any data volume connected to the edges • width or depth of greater than 60 mm • small air pockets

12. Pre-Processing and Segmentation • Segmentation automatically (S2) • Limit the remaining computations • reduces computational requirements • eliminates FPs arising within soft tissue structures • Produce a 5mm thickened region

13. CAD algorithm Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection

14. Gradient Orientation • Computes the image gradientvector • High-contrast edges • Determine the image surface normals • Reduced search space • Resulting surface normal vectors

15. CAD algorithm Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection

16. Surface Normal Overlap • Critical for detecting lesions • Convex regions and surfaces • Surface normal vectors • A dominant curvature along a single direction • polyps and nodules • Set 10mm of the projected surface normal vectors

17. Surface Normal Overlap • Robustness • Perfectly spherical objects • Radial direction • allowing roughly globular objects to have a significant response • Transverse direction • allowing nearly intersect surface normal vectors to be additive

18. CAD algorithm Pre-Processing and Segmentation Gradient Orientation Surface Normal Overlap Candidate Lesion Selection

19. Candidate Lesion Selection • Complex anatomic structures • Multiple convex surface patches • Multiple local maxima • Smallest scale of the features • Generate distinct local maxima • Set to 10 mm • Sorted in decreasing order and recorded

20. CAD algorithm

21. Outline • Introduction • CAD algorithm • Theoretical analysis • Stochastic Anatomic Shape Model • Model Parameter Estimation • Conclusion

22. Stochastic Anatomic Shape Model • A simple parametric shape • Add stochastically-governed variation • Produce realistic anatomic shape • Nominal position • Radius is random variables

23. Stochastic Anatomic Shape Model 真實的形狀 虛擬的圓形

24. Model Parameter Estimation • Performing edge detection • Identifying the surface normal vectors • nodule, polyp, vessel, fold • Finding the nominal sphere or cylinder

25. Model Parameter Estimation

26. Outline Introduction CAD algorithm Theoretical analysis Conclusion

27. Conclusion • A novel CAD algorithm • Surface normal overlap method • Theoretical traits • Statistical shape model

28. Conclusion • Optimized the performance • CT simulations • A per-lesion cross-validation method • Provided a preliminary evaluation

29. Conclusion • Ultimately envision • The first step in a larger overall detection scheme • Intensive classifier • Decrease the false positives rate

30. The End Thank you for listening.