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Innovative Surface Normal Overlap CAD Algorithm for Enhanced Detection of Colonic Polyps and Lung Nodules

This paper presents a novel Computer-Aided Detection (CAD) algorithm focusing on the Surface Normal Overlap method, aimed at improving the detection of colonic polyps and lung nodules in helical CT images. The algorithm optimally processes CT volume data for effective lesion identification while addressing challenges such as eye fatigue and accuracy concerns. Theoretical analysis and practical applications demonstrate the robustness of the method, paving the way for enhancements in medical imaging diagnostics and reduction of false positives through a detailed stochastic anatomical shape model.

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Innovative Surface Normal Overlap CAD Algorithm for Enhanced Detection of Colonic Polyps and Lung Nodules

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  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.

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