Detection and classification of masses in breast ultrasound images

Digital Signal Processing: A Review Journal

Volumn 20, Issue 3, 2010, Pages 824-836

  Shi, Xiangjun ^a   Cheng, H D ^a   Hu, Liming ^a   Ju, Wen ^a   Tian, Jiawei ^b  

^a Utah State University ^*  (United States)

^b HARBIN MEDICAL UNIVERSITY   (China)

Author keywords

Breast cancer; Breast sonography; Computer aided diagnosis (CAD); Feature selection; Fuzzy support vector machine (FSVM); Mass classification; Ultrasound (US) images

Indexed keywords

DISEASES; FEATURE EXTRACTION; IMAGE CLASSIFICATION; MEDICAL IMAGING; SUPPORT VECTOR MACHINES; ULTRASONIC IMAGING; ULTRASONOGRAPHY;

BREAST CANCER; BREAST SONOGRAPHY; COMPUTER AIDED DIAGNOSIS(CAD); FUZZY SUPPORT VECTOR MACHINE (FSVM); MASS CLASSIFICATIONS; ULTRASOUND IMAGES;

COMPUTER AIDED DIAGNOSIS;

EID: 77949489344     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2009.10.010     Document Type: Article

References (25)

1. Cancer Facts & Figures (2007), American Cancer Society
2. Chen C.C., Daponte J.S., and Fox M.D. Fractal feature analysis and classification in medical imaging. IEEE Trans. Med. Imaging 8 (1989) 133-142
3. Chen D.R., Kuo W.J., Chang R.F., Moon W.K., and Lee C.C. Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound. Ultrasound Med. Biol. 28 (2002) 897-902
4. H.D. Cheng, L. Hu, J. Tian, A.L. Sun, A novel Markov Random Field segmentation algorithm and its application to breast ultrasound image analysis, in: 6th International Conference on Computer Vision, Pattern Recognition and Image Processing, Salt Lake City, USA, 2005, pp. 644-647
5. Cheng H.D., and Shi X.J. A simple and effective histogram equalization approach to image enhancement. Digital Signal Process. 14 (2004) 158-170
6. H.D. Cheng, X.J. Shi, Mass classification based on texture features in breast ultrasound image, in: 6th International Conference on Computer Vision, Pattern Recognition and Image Processing, 2005
7. Dennis M.A., Parker S.H., Klaus A.J., Stavros A.T., Kaske T.I., and Clark S.B. Breast biopsy avoidance: The value of normal mammograms and normal sonograms in the setting of a palpable lump. Radiology 219 (2001) 186-191
8. Draper N., and Smith H. Applied Regression Analysis (1981), John Wiley and Sons
9. Friedman J., Hastie T., and Tibshirani R. The Elements of Statistical Learning (2001), Springer-Verlag, New York
10. Garra B.S., Krasner B.H., Horii S.C., Ascher S., Mun S.K., and Zeman R.K. Improving the distinction between benign and malignant breast lesions: The value of sonographic texture analysis. Ultrasound Imaging 15 (1993) 267-285
11. Haralick R.M., Shanmugam H.K., and Dinstein I. Texture features for image classification. IEEE Trans. Syst. Man Cybernet. 3 6 (1973) 610-621
12. Horsch K., Giger M.L., Venta L.A., and Vyborny C.J. Computerized diagnosis of breast lesions on ultrasound. Med. Phys. 29 (2002) 157-164
13. Huang Y.L., and Chen D.R. Support vector machines in sonography - Application to decision making in the diagnosis of breast cancer. Clinical Imaging 29 (2005) 179-184
14. Jackson V., Hendrick R., Feig S., and Kopans D. Imaging of the radiographically dense breast. Radiology 188 (1993) 297-301
15. Lefebvre F., Meunier M., Thibault F., Laugier P., and Berger G. Computerized ultrasound B-scan characterization of breast nodules. Ultrasound Med. Biol. 26 (2000) 1421-1428
16. Lin C.-F., and Wang S.-D. Fuzzy support vector machines. IEEE Trans. Neural Netw. 13 (2002) 464-471
17. Metz C.E. ROC methodology in radiological imaging. Invest. Radiol. 21 (1986) 720-733
18. Nadler M., and Smith E.P. Pattern Recognition Engineering (1993), John Wiley and Sons, New York
19. Sehgal C.M., Cary T.W., Kangas S.A., Weinstein S.P., Schultz S.M., Arger P.H., and Conant E.F. Computer-based margin analysis of breast sonography for differentiating malignant and benign masses. J. Ultrasound Med. 23 (2004) 1201-1209
20. Sivaramakrishna R., and Gordon R. Detection of breast cancer at a smaller size can reduce the likelihood of metastatic spread: A quantitative analysis. Acad. Radiol. 4 (1997) 8-12
21. Sivaramakrishna R., Powell K.A., Lieber M.L., Chilcote W.A., and Shekhar R. Texture analysis of lesions in breast ultrasound images. Comput. Med. Imaging Graph. 26 (2002) 303-307
22. Sohn C., Blohmer J.-U., and Hamper U.M. Breast Ultrasound - A Systematic Approach Technique and Image Interpretation (1999), Thieme, Stuttgart
23. Swets J.A. ROC analysis applied to the evaluation of medical imaging techniques. Invest. Radiol. 14 (1979) 109-121
24. Vapnik V.N. Statistical Learning Theory (1998), John Wiley and Sons, New York
25. G. Xiao, M. Brady, J.A. Noble, Y. Zhang, Contrast enhancement and segmentation of ultrasound images - A statistical method, in: Medical Imaging 2000: Image Processing, San Diego, CA, USA, 2000, pp. 1116-1125