A multiple classifier system for classification of breast lesions using dynamic and morphological features in DCE-MRI

Roberta Fusco, Mario Sansone, Antonella Petrillo, Carlo Sansone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we propose a Multiple Classifier System (MCS) for classifying breast lesions in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). The proposed MCS combines the results of two classifiers trained with dynamic and morphological features respectively. Twenty-one malignant and seventeen benign breast lesions, histologically proven, were analyzed. Volumes of Interest (VOIs) have been automatically extracted via a segmentation procedure assessed in a previous study. The performance of the MCS have been compared with histological classification. Results indicated that with automatic segmented VOIs 90% of test-set lesions were correctly classified.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages684-692
Number of pages9
Volume7626 LNCS
DOIs
Publication statusPublished - 2012
EventJoint IAPR International Workshops on Structural and Syntactic PatternRecognition, SSPR 2012 and Statistical Techniques in Pattern Recognition,SPR 2012 - Hiroshima, Japan
Duration: Nov 7 2012Nov 9 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7626 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherJoint IAPR International Workshops on Structural and Syntactic PatternRecognition, SSPR 2012 and Statistical Techniques in Pattern Recognition,SPR 2012
CountryJapan
CityHiroshima
Period11/7/1211/9/12

Keywords

  • breast DCE-MRI
  • morphological and dynamic features
  • multiple classification system

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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