As an important step of mass classification, mass segmentation plays an important role in computer-aided diagnosis
(CAD). In this paper, we propose a novel scheme for breast mass segmentation in mammograms, which is based on level
set method and multi-scale analysis. Mammogram is firstly decomposed by Gaussian pyramid into a sequence of images
from fine to coarse, the C-V model is then applied at the coarse scale, and the obtained rough contour is used as the
initial contour for segmentation at the fine scale. A local active contour (LAC) model based on image local information
is utilized to refine the rough contour locally at the fine scale. In addition, the feature of area and gray level extracted
from coarse segmentation is used to set the parameters of LAC model automatically to improve the adaptivity of our
method. The results show the higher accuracy and robustness of the proposed multi-scale segmentation method than the conventional ones.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.