Browse > Article
http://dx.doi.org/10.9718/JBER.2009.30.2.153

Semi-automatic System for Mass Detection in Digital Mammogram  

Cho, Sun-Il (Image & Video System lab, ICU)
Kwon, Ju-Won (Image & Video System lab, ICU)
Ro, Yong-Man (Image & Video System lab, ICU)
Publication Information
Journal of Biomedical Engineering Research / v.30, no.2, 2009 , pp. 153-161 More about this Journal
Abstract
Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.
Keywords
Mass; Semi-Automatic; Mammogram; SVM;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. Choi, Y. J. Kim, H.-R. Shin, and K.-Y. Yoo, 'Long-term prediction of female breast cancer mortality in Korea,' Asian Pacific J Cancer Prev., vol.6, pp. 16-21, 2005   ScienceOn
2 'Full-Field Digital Mammography,' Technology Evaluation Center Assessment Program, vol.17, No. 7, 2002
3 M. A. Kupinski and M. L. Giger, 'Automated seeded lesion segmentation on digital mammograms,' IEEE Trans. Med. Imag., vol. 17, pp. 510-517, 1998   DOI   ScienceOn
4 B. Zheng, G. S. Maitz, M. A. Ganott, G. Abrams, G. K. Leader, and D.Gur, 'Performance and Reproducibility of a computerized Mass Detection Scheme for Digitized Mammography Using Rotated and Resampled Images : An Assessment,' American Journal of roentgenology, vol. 185, no. 1, pp. 194-198, 2005   ScienceOn
5 U. von Luxburg, O. Bousquet, O. B. Scholkopf, 'A compression approach to support vector model selection,' The Journal of Machine Learning Research, vol. 5, pp. 293 - 323, 2004
6 Sethian, J.A., 'Fast Marching Level Set Methods for Three Dimensional Photolithography Development,' Proc. SPIE, 1996, pp. 261-272
7 B. Scholkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond, London, England, The MIT Press, 2002
8 K. Schutte and J. Glass, 'Robust detection of sonorant landmarks,' in Proc. Interspeech, 2005, pp. 1005-1008
9 V. Vapnik, Estimation of dependencies based on empirical data, New York, Springer-Verlag, 1982
10 S. M. Astley, 'Computer-based detection and prompting of mammographic abnormalities,' Br. J. Radiol., vol. 77, pp. 194- 200, 2004   DOI   ScienceOn
11 Simon Haykin, Neural networks, Upper Saddle River, NJ, : Prentice-Hall, 1999, pp. 318-350
12 J. E. Ball and L. M. Bruce, 'Level Set-Based Core Segmentation of Mammographic Masses Facilitating Three Stage (Core, Periphery, Spiculation) Analysis,' in Proc. EBMS, 2007, pp. 23-26
13 H. D. Li, M. Kallergi, L. P. Clarke, V. K. Jain, and R. A. Clark, 'Markov random field for tumor detection in digital mammography,' IEEE Trans. Med. Imag., vol. 14, pp. 565-576, 1995   DOI   ScienceOn
14 A. Agatheeswaran, 'Analysis of the effects of JPEG2000 compression on texture features extracted from digital mammograms.' Masters Thesis in Electrical and Computer Engineering. Starkville, Mississippi State University, pp. 20-37, 42-43, 2004
15 Y. Wang, X. B.Gao, and J. Li, 'A Feature Analysis Approach to Mass Detection in Mammography Based on RF-SVM,' ICIP07, vol.5, pp. 9-12, 2007
16 H. D. Cheng, X.J. Shi, R. Min, L.M. Hu, X.P. Cai, and H.N. Du, 'Approaches for automated detection and classification of masses in mammograms,' Pattern Recognition, vol. 39, pp. 646-668, 2006   DOI   ScienceOn
17 D. Cascio, F. Fauci, R. Magro, G. Raso, R. Bellotti, F. De Carlo, S. Tangaro, G. De Nunzio, M. Quarta, G. Forni, A. Lauria, M. E. Fantacci, A. Retico, G. L. Masala, P. Oliva, S. Bagnasco, S. C. Cheran, and E. Lopez Torres, 'Mammogram Segmentation by Contour Searching and Mass Lesions ClassificationWith Neural Network,' IEEE Trans. Nuclear Science, vol. 53, no. 5, pp. 2827-2833, 2006   DOI   ScienceOn
18 N. Petrick, H. P. Chan, B. Sahiner, and D. Wei, 'An adaptive density weighted contrast enhancement filter for mammographic breast mass detection,' IEEE Trans. Med. Imag., vol. 15, no. 1, pp. 59-67, 1996   DOI   ScienceOn
19 I. El-Naqa, Y. Yang, M. N. Wernick, N. P. Galatsanos, and R. M. Nishikawa, 'A support vector machine approach for detection of microcalcifications,' IEEE Trans. Med. Imag., vol. 21, pp. 1552-1563, 2002   DOI   ScienceOn
20 D. M. Catarious, A.H. Baydush, and C.E. Floyd Jr., 'Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system,' Med. Physics, vol. 31, no. 6, pp. 1512-1520, 2004   DOI   ScienceOn
21 B. Sahiner, H.-P. Chan, N. Petrick, M.A. Helvie, and M.M. Goodsitt, 'Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis,' Medical Physics, vol. 25, no. 4, pp. 516-526, 1998   DOI   ScienceOn
22 Y.J. Lee, J.M. Park, H.W. Park, 'Mammographic mass detection by adaptive thresholding and region growing,' Int. J. Imaging Systems Technol. vol. 11, no. 5, pp. 340-346, 2000   DOI   ScienceOn
23 S. Detounis, 'Computer-aided detection and second reading utility and Implementation in a high-volume breast clinic,' Applied Radiology, pp. 8-15, 2004
24 R. Campanini, D. Dongiovanni; E. Iampieri, N. Lanconelli, M. Masotti, G. Palermo, A. Riccardi, M. Roffilli, 'A novel featureless approach to mass detection in digital mammograms based on Support Vector Machines,' Physics in Medicine and Biology, vol. 49, no. 6, pp. 961-975, 2004   DOI   ScienceOn
25 D. M. Catarious, 'A Computer-Aided Detection System for Mammographic Masses.' PhD Dissertation in Biomedical Engineering. Durham, NC: Duke University, 2004
26 C. M.-Thoms, S. M. Dunn, C. F. Nodine, and H. L. Kundel, 'The perception of breast cancers - A spatial frequency analysis of what differentiates missed from reported cancers,' IEEE Trans. Medical Imaging, vol. 22, no. 10, pp. 1297-1306, 2003   DOI   ScienceOn
27 J. E. Ball, L. M. Bruce, 'Digital Mammographic Computer Aided Diagnosis (CAD) using Adaptive Level Set Segmentation,' 29th IEEE EMBS, 2007, pp. 4973-4978
28 Osher, S., and Sethian, J.A., 'Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations,' Journal of computational Physics, vol. 79, pp. 12-49, 1988   DOI   ScienceOn
29 L. Wei, Y. Yang, R. M. Nishikawa, and Y. Jiang, “A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications,” IEEE Trans. Med. Imag., vol.24, no. 3, 2005
30 S. K. Moore, 'Better breast cancer detection,' IEEE Spectrum, pp. 50-54, 2001
31 I. Christoyianni, E. Dermatas, and G. Kokkinakis, 'Fast detection of masses in computer-aided mammography,' IEEE Signal Process. Mag. vol. 17, no. 1, pp. 54-64, 2000   DOI   ScienceOn
32 J. C. Felipe, M. X. Ribeiro, E. P. M. Sousa, A. J. M. Traina, C. Jr Traina, 'Effective shape-based retrieval and classification of mammograms,' Proceedings of the ACM symposium on Applied computing, 2006, pp. 250 - 255
33 J. W. Kwon, H. K. Kang, Y. M. Ro, S. M. Kim, A Hierarchical Microcalcification Detection Algorithm Using SVM in Korean Digital Mammography, J. Biomed. Eng. Res., vol. 22, no. 10, pp.1297-1306, 2003