Automatic mass segmentation method in mammograms based on improved VFC snake model - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 174

fit to early detection of breast cancer. That is to say, the proposed approach can provide some
important basis to improve the CAD system.
This is an extension of the paper published on the IPCV'14 [ 21 ]. Here, we further evaluate
the performance of our proposed algorithm using a novel distance-based boundary similarity
measure based on the manual-segmented result mainly. In future work, we would like to clas-
sify the breast masses to benign and malignant based on the auto-segmented results of this
chapter.
References
[1] Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA
Cancer J Clin. 2011;61(2):69-90.
[2] Xu G, Li K, Feng G. Comparison of three imaging methods in the early diagnosis of
breast cancer. J Capital Med Univ. 2009;30(3):293-297.
[3] Ouyang C, Ding H, Wang G. Segmentation of masses in mammograms. Beijing Biomed
Eng. 2007;26(3):237-241.
[4] Kumar P, Sureshbabu R. Segmentation of region of interest and mass auto detection
in mammograms based on wavelet transform modulus maximum. Digit Image Process.
2011;3(7):415-421.
[5] Song E, Xu S, Xu X, Zeng J, Lan Y, Zhang S, Hung CC. Hybrid segmentation of mass
in mammograms using template matching and dynamic programming. Acad Radiol.
2010;17(11):1414-1424.
[6] Abbas Q, Celebi M, Garcia I. Breast mass segmentation using region-based and
edge-based methods in a 4-stage multiscale system. Biomed Sig Process Control.
2013;8(2):204-214.
[7] Wang Y, Tao D, Gao X, Li X, Wang B. Mammographic mass segmentation: embedding
multiple features in vector-valued level set in ambiguous regions. Patern Recogn.
2011;44(9):1903-1915.
[8] Kass M, Witkin A, Terzopoulo D. Snakes: active contour models. Int J Comput Vis.
1988;1(4):321-331.
[9] Mouelhi A, Sayadi M, Fnaiech F. A supervised segmentation scheme based on multilayer
neural network and color active contour model for breast cancer nuclei detection. In: 2013 In-
ternational conference on electrical engineering and software applications (ICEESA);
2013:1-6.
[10] Guo M, Wang Z, Ma Y, Xie W. Review of parametric active contour models in image
processing. J Conv Inf Technol. 2013;8(11):248-258.
[11] Li B, Scot T. Active contour external force using vector ield convolution for image
segmentation. IEEE Trans Image Process. 2007;16(8):2096-2106.
[12] Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer W. The digital database for screen-
ing mammography. In: The Fifth International Workshop on Digital Mammography;
2001.
[13] Suckling J, Parker J, Dance DR, Astley S, Hut I, Boggis C. The mammographic image
analysis society digital mammogram database. 1994.
[14] Chris R. Software. [Online]. Available: htp://microserf.org.uk/academic/Soft-
ware.html .
[15] Pawlak Z. Rough set approach to knowledge-based decision support. Eur J Oper Res.
1997;99(1):48-57.