Medical Image Segmentation & Computer-Aided Diagnosis

1. Medical Image Segmentation &Computer-Aided Diagnosis Wee-Kheng Leow Dept. of Computer Science National University of Singapore

2. Introduction Main medical imaging topics: • Segmentation • Classification • Quantification • Visualization Main applications: • Computer-aided diagnosis • Computer-aided surgery • Medical research

3. Introduction Research in CS Dept., NUS: • Bone contour extraction (x-ray images) • Bone fracture detection (x-ray images) • Body part segmentation (CT images) • and others Collaborate with local hospitals on: • Computer-aided diagnosis • Computer-aided surgery • Medical image & report retrieval

4. QP Query processing ( Retrieval ) User query Mining Extraction High-Level Fusion Knowledge Semantic Extraction/Interpretation Medical Ontology (UMLS, …) Stxt Semantic … Simg Svid Structural Extraction/Interpretation Ltxt Blobworld … Limg Lvid Structure Primitive Extraction Features Coulour & Texture Features Coulour & Texture Features PEtxt … PEvid Categorization Medical text Medical Image 1 … Medical Image n Medical Image Sequence Introduction

5. Introduction • Daniel Racoceanu: • Framework for medical image retrieval • Use ontology, domain knowledge, data fusion • Joo Hwee Lim: • Visual ontology approach to medical IR • Generic approach to mapping features to concepts • Caroline Lacoste: • Stochastic geometric model for image segmentation & analysis

6. Medical Image Analysis Main Approaches • Feature-based: • without model • with thresholds • Model-based: • with features • statistical model • stochastic model • shape model • physical model • Our main approach: model-based

7. Femur Contour Extraction • Femur in x-ray image. • Segmentation by model-based registration. • Basic ideas: • Find candidate landmark points. • Piecewise register model femur contour. • Run snake to get accurate contour.

8. Femur Contour Extraction • Candidatefemoral shaft • parallel lines • opposite gradients

9. Femur Contour Extraction • Candidatefemoral head • circles

10. Femur Contour Extraction • Candidateturning points • Zero-crossings of2nd derivatives

11. Femur Contour Extraction • Get landmark points • Piecewise registermodel femur contoursbetweenlandmark points

12. Femur Contour Extraction • Run snake algoto get accuratecontour • with GradientVector Flow (GVF) • with shapeconstraints

13. Femur Contour Extraction • Sample segmentation results: • success rate: 81.4% • can tolerate variations in shape, size, orientation

14. Femur Contour Extraction • Failure cases: • severely fractured femurs • healthy femurs without shaft

15. Fracture Detection • Very difficult problem: many ways to fracture. • Femoral neck fracture: most common healthy fractured

16. Fracture Detection • Radius fracture healthy fractured

17. Fracture Detection Basic ideas: • Adaptive sampling grid according to sizeof bone contour. • Extract multiple features in sampling grid: • Gaussian orientations • Markov random field • Intensity gradient • Compute difference map from mean map. • Apply probabilistic SVM (Gini-SVM). • Combine classifiers for different features.

18. Fracture Detection Features: • Gaussianorientationmap • Intensitygradientmap

19. Fracture Detection Lab tests: • Femur images: • 324 training samples, 108 testing samples • 12% are fractured • Radius images: • 71 training samples, 74 testing samples • 30% fractured

20. Fracture Detection Using various classifier combination rules:

21. Abdominal CT Segmentation • Multiple body parts in abdominal CT images. • Atlas-based segmentation by registration. • Basic ideas: • Global affine registration (rough alignment). • Iterative local affine registration. • Snake with Gradient Vector Flow.

22. Abdominal CT Segmentation • Global affine registration, based on ICP algo. atlas target

23. Abdominal CT Segmentation • Iterative local affine registration. • Use local gradientfeature to findpossiblecorrespondence. • Different affinetransformationsfor different bodyparts.

24. Abdominal CT Segmentation • Run snake with Gradient Vector Flow before after

25. Abdominal CT Segmentation • Test on 40 consecutive CT slices, 1mm thick:slices 41 to 80. • Single atlas. • Compute similarity index:normalized area overlap betweensegmented regions and ground truth.

26. Abdominal CT Segmentation • Sample segmentation results:

27. Abdominal CT Segmentation • Test results: • Average similarity > 0.9 for slices 43 to 76. • Single atlas can segment 34 slices.

28. Conclusions • Presented sample medical image analysis work in Dept. of Computer Science, NUS. • Model-based segmentation. • Detection of bone fractures in x-ray images. • International patent published under PCT. • Building prototype system for field test.

29. References • F. Ding, W. K. Leow, S.-C. Wang. Segmentation of 3D CT Volume Images Using a Single 2D Atlas. In Proc. First International Workshop on Computer Vision for Biomedical Image Applications (CVBIA2005) (in conjunction with Int. Conf. on Computer Vision, 2005). Y. Liu, T. Jiang, C. Zhang (Eds.), LNCS 3765, Springer, 2005, pp. 459-468. • Y. Chen, X. Ee, W. K. Leow, T. S. Howe. Automatic Extraction of Femur Contours from Hip X-ray Images. In Proc. First International Workshop on Computer Vision for Biomedical Image Applications (CVBIA2005) (in conjunction with Int. Conf. on Computer Vision, 2005). Y. Liu, T. Jiang, C. Zhang (Eds.), LNCS 3765, Springer, 2005, pp. 200-209. • V. L. F. Lum, W. K. Leow, Y. Chen, T. S. Howe, M. A. Png. Combining Classifiers for Bone Fracture Detection in X-Ray Images. In Proc. Int. Conf. on Image Processing, 2005. • S. E. Lim, Y. Xing, Y. Chen, W. K. Leow, T. S. Howe, and M. A. Png. Detection of Femur and Radius Fractures in X-Ray Images. In Proc. 2nd Int. Conf. on Advances in Medical Signal and Information Processing, 2004, p. 249-256.

30. References • D. W.-H. Yap, Y. Chen, W. K. Leow, T. S. Howe, and M. A. Png. Detecting Femur Fractures by Texture Analysis of Trabeculae. In Proc. Int. Conf. on Pattern Recognition, 2004, vol. 3, p. 730-733. • T. P. Tian, Y. Chen, W. K. Leow, W. Hsu, T. S. Howe, M. A. Png. Computing neck-shaft angle of femur for x-ray fracture detection. In Proc. Int. Conf. on Computer Analysis of Images and Patterns, LNCS 2756, 2003, p. 82-89.