Breast abnormality detection incorporating breast density information based on independent components analysis

Styliani Petroudi, Nicoletta Nicolaou, Julius Georgiou, Michael Brady

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper introduces an approach to breast abnormality classification which incorporates breast density information. Features are extracted by a novel technique based on Independent Component Analysis, which decomposes the selected images into sets of independent source regions and corresponding basis functions (weights). The coefficients which result from the source regions are used in turn to describe normality and abnormality. The method has been tested on the MIAS database and has high sensitivity.

Original languageEnglish
Title of host publicationDigital Mammography - 9th International Workshop, IWDM 2008, Proceedings
Pages667-673
Number of pages7
DOIs
Publication statusPublished - 9 Sept 2008
Externally publishedYes
Event9th International Workshop on Digital Mammography, IWDM 2008 - Tucson, AZ, United States
Duration: 20 Jul 200823 Jul 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5116 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Workshop on Digital Mammography, IWDM 2008
Country/TerritoryUnited States
CityTucson, AZ
Period20/07/0823/07/08

Fingerprint

Dive into the research topics of 'Breast abnormality detection incorporating breast density information based on independent components analysis'. Together they form a unique fingerprint.

Cite this