1. Segmentation of Medical Images with Regional Inhomogeneities D.K. Iakovidis, M.A. Savelonas, S.A. Karkanis + & D.E. Maroulis University of Athens Department of Informatics & Telecommunications University of Athens Department of Informatics & Telecommunications + Lamia Institute of Technology Department of Informatics & Computer Technology + Lamia Institute of Technology Department of Informatics & Computer Technology Greece

2. Homogeneity What is uniformity? A condition in which everything is regular and unvarying What is uniformity? A condition in which everything is regular and unvarying (The American Heritage® Dictionary of the English Language) What is homogeneity? The quality of being of uniform throughout in composition or structure What is homogeneity? The quality of being of uniform throughout in composition or structure

3. Regional homogeneity

4. Regional inhomogeneity

5. u 0 (x, y)

6. Aim of study Medical image segmentation Accurate delineation of abnormal findings by excluding the regional inhomogeneities Medical image segmentation Accurate delineation of abnormal findings by excluding the regional inhomogeneities

7. Aim of study The shape of a clinical finding is usually considered as a risk factor for various malignancies The shape of a clinical finding is usually considered as a risk factor for various malignancies

8. Background Pioneering studies appeared in early 1970s J. Sklansky & D. Ballard, “Tumor Detection in Radiographs,” Computers and Biomedical Research,” vol. 6, no. 4, pp. 299-321, 1973. Pioneering studies appeared in early 1970s J. Sklansky & D. Ballard, “Tumor Detection in Radiographs,” Computers and Biomedical Research,” vol. 6, no. 4, pp. 299-321, 1973.

9. Since then a variety of medical image segmentation methods have been proposed Thresholding Region growing Clustering Classification Morphological operations Fuzzy approaches Drawbacks Sensitive to noise Sensitive to inhomogeneities Since then a variety of medical image segmentation methods have been proposed Thresholding Region growing Clustering Classification Morphological operations Fuzzy approaches Drawbacks Sensitive to noise Sensitive to inhomogeneities Background

10. General review papers J.S. Duncan & N. Ayache, “Medical Image Analysis: Progress Over Two Decades and The Challenges Ahead,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 22, pp. 85-106, 2000. D. L. Pham, C. Xu & J. L. Prince, “Current Methods in Medical Image Segmentation,” Annual Review of Biomedical Engineering, vol. 2, pp. 315-338, 2000. General review papers J.S. Duncan & N. Ayache, “Medical Image Analysis: Progress Over Two Decades and The Challenges Ahead,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 22, pp. 85-106, 2000. D. L. Pham, C. Xu & J. L. Prince, “Current Methods in Medical Image Segmentation,” Annual Review of Biomedical Engineering, vol. 2, pp. 315-338, 2000. Background

11. State of the art approaches for shape recovery in medical images include the level set deformable models Special review papers J.S. Suri et al., “Shape Recovery Algorithms Using Level Sets in 2-D/3-D Medical Imagery: A State-of-the-Art Review”, ΙΕΕΕ Trans. on Inf. Tech. in Biomedicine, vol. 6, no. 1, pp. 8-28, Mar. 2002. T. McInerney & D. Terzopoulos, “Deformable Models in Medical Image Analysis: A Survey,” Med Image Analysis, vol. 1, pp. 91-108, 1996. State of the art approaches for shape recovery in medical images include the level set deformable models Special review papers J.S. Suri et al., “Shape Recovery Algorithms Using Level Sets in 2-D/3-D Medical Imagery: A State-of-the-Art Review”, ΙΕΕΕ Trans. on Inf. Tech. in Biomedicine, vol. 6, no. 1, pp. 8-28, Mar. 2002. T. McInerney & D. Terzopoulos, “Deformable Models in Medical Image Analysis: A Survey,” Med Image Analysis, vol. 1, pp. 91-108, 1996.

12. Deformable models Deformation of initial contours Initial contours are deformed towards the boundaries of the image regions to be segmented Energy functional minimization The functional is designed so that its global minimum is reached at the target boundaries Deformation of initial contours Initial contours are deformed towards the boundaries of the image regions to be segmented Energy functional minimization The functional is designed so that its global minimum is reached at the target boundaries

13. Deformable surface The surface is a function  (x, y) of image intensities Contours are obtained for  (x, y) = 0 The contours are formed by the zero level set of  (x, y) Deformable surface The surface is a function  (x, y) of image intensities Contours are obtained for  (x, y) = 0 The contours are formed by the zero level set of  (x, y) Level-Set deformable models

14. Contour Energy functional Seek for Contour Energy functional Seek for Active Contour Without Edges model (Chan & Vesse, 2001)

15. Finally  (x, y) is obtained by solving Basic assumption The image u 0 is formed by two regions of approximately piecewise-constant intensities of distinct values c 1 and c 2. Finally  (x, y) is obtained by solving Basic assumption The image u 0 is formed by two regions of approximately piecewise-constant intensities of distinct values c 1 and c 2. Active Contour Without Edges model (Chan & Vesse, 2001)

16. The need for a new model How piecewise-constant really are the intensities of medical images with regional inhomogeneities? How piecewise-constant really are the intensities of medical images with regional inhomogeneities?

17. The proposed model Coping with regional inhomogeneities We propose a new model that considers sparse foreground and background from which the regional inhomogeneities are excluded. Coping with regional inhomogeneities We propose a new model that considers sparse foreground and background from which the regional inhomogeneities are excluded.

18. The proposed model Recall that  (x, y) is obtained by solving where

19. The proposed model Recall that  (x, y) is obtained by solving where

20. The proposed model The delta term is estimated as follows for

21.  (x, y)

23. The proposed model Recall that  (x, y) is obtained by solving where

24. The proposed model Recall that  (x, y) is obtained by solving where

25. Setup of the experiments 38 medical images Endoscopic: gastric ulcers Ultrasound: parotid tumors & thyroid nodules Image properties Dimensions 256x256 pixels Grey level depth 8-bit Segmentation quality measure Overlap value 38 medical images Endoscopic: gastric ulcers Ultrasound: parotid tumors & thyroid nodules Image properties Dimensions 256x256 pixels Grey level depth 8-bit Segmentation quality measure Overlap value

26. Summary of results

27. Output images 96.4% 98.1% 94.2% 88.4%

28. Output video 1/3 Thyroid nodule and small initial contour 89.2% 3 min 89.2% 3 min

29. Output video 2/3 Thyroid nodule and large initial contour 98.9% 47 sec 98.9% 47 sec

30. Output video 3/3 98.1% 47 sec 98.1% 47 sec Gastric ulcer

31. Conclusions We have introduced a novel, improved deformable model based on the ACWE model The new model assumes piecewise constancy over sparse regions outside and inside the active contour was applied for the segmentation of medical images with regional inhomogeneities outperforms the ACWE model in the delineation of abnormal tissue masses works better for images with more prevalent regional inhomogeneities We have introduced a novel, improved deformable model based on the ACWE model The new model assumes piecewise constancy over sparse regions outside and inside the active contour was applied for the segmentation of medical images with regional inhomogeneities outperforms the ACWE model in the delineation of abnormal tissue masses works better for images with more prevalent regional inhomogeneities

32. Future work Systematic evaluation on larger datasets of medical images acquired on regular basis Investigation of automatic parameter tuning approaches Integration of the proposed model in a medical decision support system Systematic evaluation on larger datasets of medical images acquired on regular basis Investigation of automatic parameter tuning approaches Integration of the proposed model in a medical decision support system

33. Thank you