By Topic

Breast cancer detection using image processing techniques

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
T. C. Cahoon ; Dept. of Comput. Sci., Univ. of West Florida, Pensacola, FL, USA ; M. A. Sutton ; J. C. Bezdek

We describe the use of segmentation with fuzzy models and classification by the crisp k-nearest neighbor (k-nn) algorithm for assisting breast cancer detection in digital mammograms. Our research utilizes images from the digital database for screening mammography. We show that supervised and unsupervised methods of segmentation, such as k-nn and fuzzy c-means, in digital mammograms will have high misclassification rates when only intensity is used as the discriminating feature. Adding window means and standard deviations to the feature suite (visually) improves segmentation produced by the k-nn rule. While our results are encouraging, other methods are needed to detect smaller pathologies such as microcalcifications

Published in:

Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on  (Volume:2 )

Date of Conference: