Mammographic mass segmentation based on maximum entropy principle and active contour model

Enmin Song; Luan Jiang; Jinhui Liu; Renchao Jin; Xiangyang Xu

doi:10.1117/12.751073

14 November 2007 Mammographic mass segmentation based on maximum entropy principle and active contour model

Enmin Song, Luan Jiang, Jinhui Liu, Renchao Jin, Xiangyang Xu

Proceedings Volume 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 67891G (2007) https://doi.org/10.1117/12.751073
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Mammographic mass segmentation plays a crucial role in computer-aided scheme (CAD). In this paper, we propose a method based on maximum entropy principle and active contour model to do segmentation. There are two main steps in this method. First, maximum entropy principle was applied on the background-trend corrected regions of interest (ROIs) to obtain the initially detected outlines. Secondly, active contour model was used to refine the initially detected outlines of the masses. The regions of interest used in this study were extracted from images in the Digital Database for Screening Mammography (DDSM) provided by the University of South Florida. The preliminary experimental results are encouraging. The segmentation algorithm performs robustly and well for various types of masses. The overlap criterion analysis shows that the proposed segmentation results are more similar to radiologists' manual segmentation compared with other experimented methods.

Citation Download Citation

Enmin Song, Luan Jiang, Jinhui Liu, Renchao Jin, and Xiangyang Xu "Mammographic mass segmentation based on maximum entropy principle and active contour model", Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 67891G (14 November 2007); https://doi.org/10.1117/12.751073

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available