By Topic

Edge detection in mammogram images using log-normal distribution

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

2 Author(s)
Ali El-Zaart ; Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University - Lebanon ; Wafaa Kamel Al-Jibory

A mammography exam, called a mammogram, is an important examination aid that is designed to help human in the early detection and diagnosis of breast diseases especially in women. Image processing is using for detecting for objects in mammogram images. Edge detection; which is a method of determining the discontinuities in gray level images; is a very important initial step in Image processing. Many classical edge detectors have been developed over time. Some of the well known edge detection operators based on the first derivative of the image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image with masks. Also Gaussian distribution has been used to build masks for the first and second derivative. However, this distribution has limit to only symmetric shape. This paper will use to construct the masks, the log-normal distribution which was more general than Gaussian because it has symmetric and asymmetric shape. The constructed masks are applied to images and we obtained good results.

Published in:

Advances in Computational Tools for Engineering Applications (ACTEA), 2012 2nd International Conference on

Date of Conference:

12-15 Dec. 2012