Physics based contrast marking and inpainting based local texture comparison for clustered microcalcification detection

Lecture Notes in Computer Science

Volumn 3217, Issue 1 PART 2, 2004, Pages 847-855

  Yuan, Xin ^a,b   Shi, Pengcheng ^b  

^a ZHEJIANG UNIVERSITY   (China)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL ORGANS; BLOOD VESSELS; COMPUTER AIDED DESIGN; DATABASE SYSTEMS; MEDICAL IMAGING; MORPHOLOGY; TISSUE; TUMORS; ADAPTIVE BOOSTING; CLUSTERING ALGORITHMS; COMPUTER AIDED DIAGNOSIS; DIAGNOSIS; MAMMOGRAPHY; MEDICAL COMPUTING;

IMAGING PROCESSES; MAMMOGRAMS; MICROCALCIFICATION; X-RAY IMAGING; BACKGROUND TEXTURES; CLUSTERED MICROCALCIFICATION DETECTIONS; CLUSTERED MICROCALCIFICATIONS; COMPUTER AIDED DIAGNOSIS SYSTEMS; IMAGE ANALYSIS TECHNIQUES; LOCAL CHARACTERISTICS; MICROCALCIFICATIONS; NONLINEAR FUNCTIONS;

IMAGE ANALYSIS; CALCIFICATION (BIOCHEMISTRY);

EID: 20344407544     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30136-3_103     Document Type: Conference Paper

References (20)

1. Digital mammography database of the mammographic image analysis society. http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html.
2. S. Astley, I. Hutt, et al. Automation in mammography: Computer vision and human perception. In State of The Art in Digital Mammographie Image Analysis, pages 1-25. World Scientific, 1994.
3. T. Bhangale, U.B. Desai, and U. Sharma. An unsupervised scheme for detection of microcalcifications on mammograms. In IEEE ICIP, pages 184-187, 2000.
4. G. Boccignone, A. Chianese, and A. Picariello. Using Renyi's information and wavelets for target detection: An application to mammograms. 3:303-313, 2000.
5. H.P. Chan, S.C. B. Lo, B. Sahiner, K.L. Lam, and M.A. Helvie. Computer-aided detection of mammographie microcalcifications: Pattern recognition with an artificial neural network. Medical Physics, 22(10):1555-1567, 1995.
6. T.F. Chan and J. Shen. Inpainting based on nonlinear transport and diffusion. In Inverse problems, image analysis, and medical imaging, pages 53-65. 2002.
7. Y. Chitre, A.P. Dhawan, and M. MoskoWitz. Artificial neural network based classification of mammographie microcalcifications using image structure features. In State of The Art in Digital Mammographie Image Analysis, pages 167-197. World Scientific, 1994.
8. I. El-Naqa, Y. Yang, M.N. Wernick, N.P. Galatsanos, and R. Nishikawa. A support vector machine approach for detetcion of microcalcifications in mammograms. In IEEE ICIP, pages 201-204, 2002.
9. Y. Preund and R.E. Schapire. Experiments with a new boosting algorithm. In International Conference on Machine Learning, pages 148-156, 1996.
10. T.O. Gulsrud, J.H. Husøy, and H. Stavanger. Optimal filter for detection of clustered microcalcifications. In ICPR, volume 1, pages 508-511, 2000.
11. J. Jia and C.K. Tang. Image repairing: robust image synthesis by adaptive nd tensor voting. In IEEE CVPR, pages 643-650, 2003.
12. S. Mallat. A Wavelet Tour of Signal Processing. Academic Press, 1998.
13. T. Netsch. Detection of micro calcification clusters in digital mammograms: A scale space approach. In IWDM, 1996.
14. R.N. Strickland and H.I. Hahn. Wavelet transforms for detecting microcalcifications in mammograms. IEEE TMI, 15(2):218-229, 1996.
15. V.N. Vapnik. The Nature of Statistical Learning Theory. Springer, 2000.
16. W.J.H. Veldkamp and N. Karssemeijer. Accurate segmentation and contrast measurement of mes in mammograms: a phantom study. Medical Physics, 25(7): 1102-1110, 1998.
17. W.J.H. Veldkamp and N. Karssemeijer. Normalization of local contrast in mammograms. IEEE TMI, 19(7):731-738, 2000.
18. Margaret Yam. Detection and Analysis of Microcalcification Clusters in X-ray Mammograms Using the hint Representation. PhD thesis, University of Oxford, 2001.
19. X. Yuan and P.C. Shi. The physical nature of mammographie image processing. Technical report, Biomedical Research Laboratory, Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology, 2003.
20. O.R. Zaïane, M.-L. Antonie, and A. Coman. Mammography classification by an association rule-based classifier. In ACM SIGKDD: International Workshop on Multimedia Data Mining, pages 62-69, 2002.