Automatic lesion detection in breast DCE-MRI

Stefano Marrone, Gabriele Piantadosi, Roberta Fusco, Antonella Petrillo, Mario Sansone, Carlo Sansone

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

Abstract

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated in recent years a great potential in screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. The aim of this work is to propose an automated system for suspicious lesion detection in DCE-MRI to support radiologists during patient image analysis. The proposed method is based on a Support Vector Machine trained with dynamic features, extracted, after a suitable pre-processing of the image, from an area pre-selected by using a pixel-based approach. The performance were evaluated by using a leave-one-patient-out approach and compared to manual segmentation made up by an experienced radiologist. Our results were also compared to other automatic segmentation methodologies: the proposed method maximises the area of correctly detected lesions while minimizing the number of false alarms (with an accuracy of 98.70%).

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages359-368
Number of pages10
Volume8157 LNCS
EditionPART 2
DOIs
Publication statusPublished - 2013
Event17th International Conference on Image Analysis and Processing, ICIAP 2013 - Naples, Italy
Duration: Sep 9 2013Sep 13 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8157 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other17th International Conference on Image Analysis and Processing, ICIAP 2013
CountryItaly
CityNaples
Period9/9/139/13/13

Keywords

  • DCE-MRI
  • dynamic features
  • ROI detection
  • SVM

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Marrone, S., Piantadosi, G., Fusco, R., Petrillo, A., Sansone, M., & Sansone, C. (2013). Automatic lesion detection in breast DCE-MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 8157 LNCS, pp. 359-368). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8157 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-41184-7_37