Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines

Neural Computing and Applications

Volumn 15, Issue 2, 2006, Pages 164-169

  Huang, Yu Len ^a   Wang, Kao Lun ^b   Chen, Dar Ren ^c  

^a TUNGHAI UNIVERSITY   (Taiwan)

^b CHUNG SHAN MEDICAL UNIVERSITY HOSPITAL   (Taiwan)

^c CHANGHUA CHRISTIAN HOSPITAL   (Taiwan)

Author keywords

Breast cancer; Computer aided diagnosis; Multilayer perception; Support vector machine; Textural analysis; Ultrasound

Indexed keywords

EID: 31944443382     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-005-0019-5     Document Type: Article

References (19)

1. American Cancer Society (2003) Breast cancer facts and figures 2001-2002. American Cancer Society, Atlanta, Georgia
2. Stavros AT, Thickman D, Rapp CL, Dennis MA, Parker SH, Sisney GA (1995) Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology 196(1); 123-134
3. Bassett LW, Liu TH, Giuliano AE, Gold RH (1991) The prevalence of carcinoma in palpable vs impalpable, mammographically detected lesions. AJR Am J Roentgenol 157(1):21-24
4. Chen D, Chang RF, Huang YL (2000) Breast cancer diagnosis using self-organizing map for sonography. Ultrasound Med Biol 26(3):405-411
5. Chen DR, Chang RF, Huang YL, Chou YH, Tiu CM, Tsai PP (2000) Texture analysis of breast tumors on sonograms. Semin Ultrasound CT MR 21(4):308-316
6. Chen DR, Chang RF, Kuo WJ, Chen MC, Huang YL (2002) Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound Med Biol 28(10):1301-1310
7. Chen DR, Chang RF, Huang YL (1999) Computer-aided diagnosis applied to US of solid breast nodules by using neural networks. Radiology 213(2):407-412
8. Garra BS, Krasner BH, Horii SC, Ascher S, Mun SK, Zeman RK (1993) Improving the distinction between benign and malignant breast-lesions-the value of sonographic texture analysis. Ultrason Imaging 15(4):267-285
9. Haykin S (1999) Multilayer perceptrons. In: Marcia Horton (ed) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall, New Jersey, pp. 156-255
10. Yang DC, Sang YS (2003) Image retrieval using BDIP and BVLC moments. IEEE Trans Circuits Syst Video Technol 13(9):951-957
11. Christianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, UK
12. Kim KI, Jung K, Park SH, Kim HJ (2002) Support vector machines for texture classification. IEEE Trans Pattern Anal Mach Intell 24(11): 1542-1550
13. Song Q, Hu WJ, Xie WF (2002) Robust support vector machine with bullet hole image classification. IEEE Trans Syst Man Cyber C Appl Rev 32(4):440-448
14. El Naqa I, Yang YY, Wernick MN, Galatsanos NP, Nishikawa RM (2002) A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging 21(12): 1552-1563
15. Yang MH, Roth D, Ahuja N (2002) A tale of two classifiers: SNoW vs. SVM in visual recognition. Comput Vis - ECCV 2353(Pt IV):685-699
16. Sun YF, Fan XD, Li YD (2003) Identifying splicing sites in eukaryotic RNA: support vector machine approach. Comput Biol Med 33(1):17-29
17. Song MH, Breneman CM, Bi JB, Sukumar N, Bennett KP, Cramer S et al (2002) Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J Chem Inf Comput Sci 42(6): 1347-1357
18. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29-36
19. Weiss SM, Kapouleas I (1989) An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. Proceedings of the 11th international joint conference on artificial intelligence. Morgan Kaufmann, Detroit, pp. 781-787