PCNN for detection of masses in digital mammogram

Title:

PCNN for detection of masses in digital mammogram

Creator:

Jafar M. H., Ali and Hassanien, Aboul Ella

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:acf19783-08eb-4201-85da-b31c27b0ac80
uuid:acf19783-08eb-4201-85da-b31c27b0ac80

Subject:

Fuzzy image processing, PCNN, mammogram analysis, enhancement, and segmentation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The pulse-coupled neural network (PCNN) is a neural network that has the ability to extract edges, image segments and texture information from images. Only a few changes to the PCNN parameters are necessary to effective operating on different types of data. This is an advantage over the published image segmentation algorithms which generally require information about the target before they are effective. This paper introduces the PCNN algorithm to provide an accurate segmentation of potential masses in mammogram images to assist radiologists in making their decisions. The fuzzy histogram hyperbolization algorithm is first applied to increase the contrast of the mammogram image before reasonable segmentation. It is followed by the PCNN algorithm to extract the region of interest to arrive at the final result. To test the effectiveness of the introduces algorithm on high quality images, a set of mammogram images was chosen and obtained from the Digital Databases for Mammography Image Analysis Society (MIAS). Four measures of quantifying enhancement have been adapted in this work. Each measure is based on the statistical information obtained from the labeled region of interest and a border area surrounding it. A comparison with the fuzzy c-mean clustering algorithm has been made.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2006 Volume:16 | Number:2

Harvested from:

CDK

Metadata only:

false