Automatic segmentation and diagnosis of breast lesions using morphology method based on ultrasound

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3614, Issue PART II, 2005, Pages 1079-1088

  Jung, In Sung ^a   Thapa, Devinder ^a   Wang, Gi Nam ^a  

^a Ajou University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DIAGNOSIS; NUMERICAL ANALYSIS; TUMORS;

LESIONS; MULTI-STANDARD DEVIATION; REGION OF INTEREST (ROI); TUMOR PROFILING;

IMAGE SEGMENTATION;

EID: 26944483120     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11540007_139     Document Type: Conference Paper

References (20)

1. kyoung,Moon,woo :Screening of Breast . Lecture note in Breast cancer image, Seoul National University. (2004)1-8
2. Ministry of health and welfare :www.ncc.re.kr. Korean cancer report (2001.1.1-2001.12.31),(2003)
3. Karvonen,J.,A.: Baltic Sea Ice SAR Segmentation and Classification Using Modified Pulse-Coupled Neural Networks . IEEE Trans. on Geoscience and Remote Sensing, vol.42, No.7, (2004)
4. Bick,U. ,Giger, M., L. , Schmidt.R. ,A., and Doi,K.: A new single-image method for computer-aided detection of small mammography masses.Proceedings of CAR 95, (1995) 357-363.
5. Tsai,D.,Y. , et al.: A Computer - Aided System for Discrimination of Dilated Cardiomyopathy Using Echocardiograph Images. IEICE Trans. Fundamentals,Vol. E78-A,(1995)1649-1654
6. Tsai,D.,Y. , et al.: Comparative Performance Study of BP-and GA-based Neural Networks for Automated Classification of heart Diseases from Ultrasound Images. CAR'98 Computer Assisted radiology and Surgery,(1998) 248-253
7. Haykin,Simon : Neural Netwroks A Comprehensive Foundation. Prentice Hall International. Inc.,(1999)156-479
8. Jung,In-Sung, and Wang,Gi-Nam : Development of an adaptive-intelligent CAD framework . Proceedings of HCI 2004 , (2003)
9. Jung,In-Sung, and Wang,Gi-Nam : CAD system framework. Proceedings of HCI 2004, (2003)
10. Acharya,M.,De,R.,K., and Kundu,M.,K.: Extraction of Features Using M-Band Wavelet Packet Frame and Their Neuro-Fuzzy Evaluation for Multitexture Segmentation . IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.20, No. 12, December (2003)
11. Horsch,K.,M., Gifer,L. ,Luz, A.,V., Vyborny,C., J. : Computerize diagnosis of breast lesions on ultrasound. Medical Physics, Vol. 29, No. 2, (2002)157-164
12. Horsch,K.,M., Gifer,L. ,Luz, A.,V., Vyborny,C., J. : Automatic segmentation of breast lesions on ultrasound . Medical Physics, Vol. 29, No. 2, (2002)
13. Stavros,T., Thickman,D., Ra,C. ,L., Dennis,M. ,A. ,Parker, S., H., and Sisney,G., A. : Solid breast nodule. Use of sonography to distinguish between benign and malignant lesions. Radiology 196, (1995) 123-134.
14. Giger,M. ,L. ,Al-Hallaq, H. ,Huo, Z., Moran,C. , Wolverton,D., E.,Chan, C., W., and Zhong,W. : Computerized analysis of lesions in us image of the breast. Acad. Radiol 6, (1999) 665-674
15. Garra,S. , Krasner,B., H., Horii,S., C., Ascher,S. , Mun,S., K., and Zeman,P., K. : Improving the distinction between benign and malignant breast lesion. The value of sonographic texture analysis. Ultrason. Imaging 15, (1993) 267-285
16. Golub,R., M., et al.: Differentiation of breast tumors by ultrasonic tissue characterization. J. Ultrasound Med . 12, (2004) 601-608
17. Sahiner,B., et al.: Computerize characterization of breast masses three-dimensional ultrasound images. in proceeding of the SPIE (SPIE, Bellingham,WA,1988, Vol.3338), (1988)301-312
18. Tohno,E. , Cosgrove,D., O., and Sloane.J., P.: ultrasound Diagnosis of Breast Disease . ChurchillLivingstone, Edinburgh, Scotland,(1994)
19. Huang,S.,F. , Chang,R.,F., Member, IEEE, Chen,D.,R., and Moon,W. K. :Characterization of Spiculation on Ultrasound Lesions .IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 23 , (2004), 1 111-121
20. Otsu,N. : A threshold selection method from gray-level histograms .IEEE Trans. Syst. Man Cybern., vol. SMC-9, (1979), 62-66