Segmentation and classification of breast lesions using dynamic and textural features in dynamic contrast enhanced-magnetic resonance imaging

Roberta Fusco, Mario Sansone, Carlo Sansone, Antonella Petrillo

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

Abstract

The aim of this study is to propose an approach, based on Multi Layer Perceptron classification of dynamic and textural features, for breast lesions segmentation and classification using Dynamic Contrast Enhanced-Magnetic Resonance Imaging data. We compared the performance obtainable with dynamic, textural and spatio-temporal features. In particular, 98 dynamic features, 60 textural features and 72 spatio-temporal features were considered. The dataset included 20 breast lesions, 10 benign and 10 malignant. The performance of lesion segmentation have been evaluated with respect to manual segmentation provided by an expert radiologist. Results of lesion classification were compared to histological findings. Our results indicate that Multi Layer Perceptron can achieve better results in terms of sensitivity, specificity and accuracy when dynamic features are considered both for lesion segmentation and classification (accuracy of 91 % and 70 %, respectively).

Original languageEnglish
Title of host publicationProceedings - IEEE Symposium on Computer-Based Medical Systems
DOIs
Publication statusPublished - 2012
Event25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 - Rome, Italy
Duration: Jun 20 2012Jun 22 2012

Other

Other25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012
CountryItaly
CityRome
Period6/20/126/22/12

ASJC Scopus subject areas

  • Computer Science Applications
  • Radiology Nuclear Medicine and imaging

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