Fuzzy systems and deformable models

Intelligent and Adaptive Systems in Medicine

Volumn , Issue , 2008, Pages 305-329

  Bueno, Gloria ^a  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77954655305     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (46)

1. Duncan J.S. and Ayache N., Medical image analysis: Progress over two decades and the challenges ahead, IEEE Trans. PAMI, 22, 85-106, 2000.
2. Nguyen H.T. and Sugeno M., Fuzzy Systems: Modeling and Control, Kluwer, Norwell, MA, 1998.
3. Klir G. and Yuan B., Fuzzy Sets and Fuzzy Logic Theory and Applications, Prentice Hall, Englewood Cliffs, NJ, 1995.
4. McInerney T. and Terzopoulos D., Deformable models in medical image analysis: A survey, Med. Image Anal, 2(1), 91-108, 1996.
5. Solaiman B., Debon R., Pipelier F., Cauvin J.-M., and Roux C., Information fusion: Application to data and model fusion for ultrasound image segmentation, IEEE Trans. Biomed. Eng., 46(10), 1171-1175, 1999.
6. Zadeh L.A., Fuzzy sets, Inf. Control, 8, 338-335, 1965.
7. Mohamed N., A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data, IEEE Trans. Med. Imag., 21(3), 193-200, 2002.
8. Kass M., Witkin A., and Terzopoulos D., Snakes: Active contour models, Int. J. Comput. Vis, 14(26), 321-331, 1998.
9. Ray N., Havlicek J., Acton S.T., and Pattichis M., Active contour segmentation guided by AM-FM dominant componente analysis, IEEE Int. Conf. Image Process, 1, pp. 78-81, 2001.
10. Yu Z. and Bajaj C., "Image segmentation using gradient vector diffusion and region merging, IEEE International Conference on Pattern Recognition, Quebec, Canada, (11-15) August, 2, pp. 941-944, 2002.
11. Malladi R., Sethian J.A., and Vemuri B.C., Shape modeling with front propagation: A level set approach, IEEE Trans. PAMI, 17, 158-175, 1995.
12. Wang H. and Ghosh B., Geometric active deformable models in shape modeling, IEEE Trans. Image Process, 9(2), 302-308, 2000.
13. Caselles V., Kimmel R., and Sapiro G., Geodesic active contours, Int. J. Comput. Vis., 22(1), 61-79, 1997.
14. Dubrovin B.A., Fomenko A.T., and Novikov S.P., Modern Geometry, Methods and Applications I, Springer-Verlag, Heidelberg, 1984.
15. Nastar C. and Ayache N., Frequency-based nonrigid motion analysis: Application to four-dimensional medical images, IEEE Trans. Pattern Anal. Machine Intell, 18, 1069-1079, 1996.
16. Borgefors G., On digital distance tranforms in three dimensions, Comput. Vision Image Understanding, 64(3), 368-376, 1996.
17. Pentland A. and Sclaroff S., Closed-form solutions for physically based shape modeling and recognition, IEEE Trans. Pattern Anal. Machine Intell., 13, 730-742, 1991.
18. Taylor P., Champness J., Given-Wilson R., Johnston K., and Potts H., Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography, Health Technol. Assess., 9(6), 1-58, 2005.
19. Alberdi E., Povykalo A., Strigini L., and Ayton P., Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography, Acad. Radiol., 11(8), 909-918, 2004.
20. Jiang Y., Nishikawa R., Schmidt R., Toledano A., and Doi K., Potencial of computer-aided diagnosis to reduce variability in radiologist interpretations of mammograms depicting microcalcifications, Radiology, 220, 787-794, 2001.
21. Petrick N., Sahiner B., Chan H., Helvie M., Paquerault S., and Hadjiiski L., Breast cancer detection: Evaluation of a mass-detection algorithm for computer-aided diagnosis-experience in 263 patients, Radiology, 224, 217-224, 2002.
22. Cheng H.D., Chen C.H., and Chiu H.H., Fuzzy homogeneity approach to image thresholding and segmentation, Inform. Sci.: An Int. J, 98(1-4), 237-262, 1997.
23. Mendez A.J., Tahoces P.G., Lado M.J., and Soute M., Computer-aided diagnosis: Texture features to discriminate between malignant masses and normal breast tissue in digitized mammograms, CAR'97 Comput. Aided Radiol. Int. Conf., 1, 342-346, 1997.
24. Pham D.L. and Prince J.L., Adaptive fuzzy segmentation of magnetic resonance images, IEEE Trans. Med. Imag., 18(9), 737-752, 1999.
25. Polakowski W.E., Cournoyer D.A., Rogers S.K., DeSimio M.P., Ruck D.W., Hoffmeister J.W., Raines R.A., Computer-aided breast cancer detection and diagnosis of masses using difference of gaussians and derivative-based feature saliency, IEEE Trans. Med. Imag., 16(6), 811-819, 1997.
26. Kobatake H. et al., Computerized detection of malignant tumors on digital mammograms, IEEE Trans. Med. Imag., 18(5), 369-378, 1999.
27. Ferrari R.J. et al., Analysis of asymmetry in mammograms via directional filtering with gabor wavelets, IEEE Trans. Med. Imag., 20(9), 953-964, 2001.
28. Mammography Image Analysis Society, Digital Mammogram Database, Manchester, U.K., 1994.
29. Haas O., Radiotherapy Treatment Planning. New System Approaches, Springer-Verlag, Heidelberg, 1998.
30. Purdy J.A., 3D treatment planning and intensity-modulated radiation therapy, Oncology, 13, 155-168, 1999.
31. Bueno G., Fisher M., Burnham K., and Haas O., Automatic segmentation of clinical structures for RTP: Evaluation of a morphological approach, MIAU Int. Conf. U.K., 22, 73-76, 2001.
32. Tsagaan B., Shimizu A., Kobatake H., Miyakawa K., and Hanzawa Y., Segmentation of kidney by using a deformable model, Proc. Int. Conf. Image Process., 3, 1059-1062, 2001.
33. Koss J.E, Newman F.D., Johnson T.K., and Kirch D.L., Abdominal organ segmentation using texture transform and a Hopfield neural network, IEEE Trans. Med. Imag., 18(7), 640-648, 1999.
34. Lee C., Chung P., and Tsai H., Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules, IEEE Trans. Inf. Technol. Biomed, 7(3), 208-217, 2003.
35. Barthe J., Methods and advanced equipment for simulation and treatment in radio oncology, http://www.maestro-research.org/index.htm, 2005.
36. Ivins J. and Porrill J., Active region models for segmenting medical images, IEEE Trans. Image Process., 227-231, 1994.
37. Martin J., Pentland A., Sclaro S., and Kikinis R., Characterization of neuro-pathological shape deformations, IEEE Trans. Pattern Anal. Machine Intell., 20(2), 97-112, 1998.
38. Gee J., Reivich M., and Bajcsy R., Elastically deforming 3D atlas to match anatomical brain images, J. Comput. Assist. Tomogr., 17(2), 225-236, 1993.
39. Zeng X., Staib L., Schultz R., and Duncan J., Segmentation and measurement of the cortex from 3D MR Images using coupled-surfaces propagation, IEEE Trans. Med. Imag., 18(10), 927-937, 1999.
40. Thompson P., MacDonald D., Mega M., Holmes C., Evans A., and Toga A., Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces, J. Comput. Assist. Tomogr., 21(4), 567-581, 1997.
41. Bueno G., Heitz F., and Armspach J.P., 3D segmentation of anatomical structures in MR images on large data sets, Magn. Reson. Imag., 19, 73-88, 2001.
42. Nikou C., Armspach J.P., Heitz F., Namer I.J., and Grucker D., MR/MR and MR/SPECT registration of brain images by fast stochastic optimization of robust voxel similarity measures, Neuroimage, 8(1), 30-43, 1998.
43. Bueno G., Nikou C., Heitz F., and Armspach J.P., Construction of a 3D physically-based multi-object deformable model, ICIP 2000, Proc. IEEE Int. Conf. Image Process., 268-271, 2001.
44. Nikou C., Bueno G., Heitz F., and Armspach J.P., A joint physics-based statistical deformable model for multimodal brain image analysis, IEEE Trans. Med. Imag., 20(10), 1026-1037, 2001.
45. Declerck J., Subsol G., Thirion J.P., and Ayache N., Automatic retrieval of anatomical structures in 3D medical images, Technical Report 2485, INRIA, 1995.
46. Kelemen A., Szekely G., and Gerig G., Elastic model-based segmentation of 3D neuroradiological data sets, IEEE Trans. Med. Imag., 18(10), 828-839, 1999.