Wang, Y. and Tao, D. and Gao, X. and Li, Xuelong and Wang, B. (2011) Mammographic mass segmentation: embedding multiple features in vector-valued level set in ambiguous regions. Pattern Recognition 44 (9), pp. 1903-1915. ISSN 0031-3203.
Abstract
Mammographic mass segmentation plays an important role in computer-aided diagnosis systems. It is very challenging because masses are always of low contrast with ambiguous margins, connected with the normal tissues, and of various scales and complex shapes. To effectively detect true boundaries of mass regions, we propose a feature embedded vector-valued contour-based level set method with relaxed shape constraint. In particular, we initially use the contour-based level set method to obtain the initial boundaries on the smoothed mammogram as the shape constraint. To prevent the contour leaking and meanwhile preserve the radiative characteristics of specific malignant masses, afterward, we relax the obtained shape constraint by analyzing possible valid regions around the initial boundaries. The relaxed shape constraint is then used to design a novel stopping function for subsequent vector-valued level set method. Since texture maps, gradient maps, and the original intensity map can reflect different characteristics of the mammogram, we integrate them together to obtain more accurate segmentation by incorporating the new stopping function into the newly proposed feature embedded vector-valued contour-based level set method. The experimental results suggest that the proposed feature embedded vector-valued contour-based level set method with relaxed shape constraint can effectively find ambiguous margins of the mass regions. Comparing against existing active contours methods, the new scheme is more effective and robust in detecting complex masses.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | mass segmentation, computer-aided diagnose, vector-valued level set, relaxed shape constraint, mammograms |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 20 Jun 2013 09:37 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7511 |
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