Detecting abnormal mammographic cases in temporal studies using image registration features

Robert Martí, Yago Díez, Arnau Oliver, Meritxell Tortajada, Reyer Zwiggelaar, Xavier Lladó

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

3 Citations (Scopus)

Abstract

Image registration is increasingly being used to help radiologists when comparing temporal mammograms for lesion detection and classification. This paper evaluates the use of image and deformation features extracted from image registration results in order to detect abnormal cases with masses. Using a dataset of 264 mammographic images from 66 patients (33 normals and 33 with masses) results show that the use of a non-rigid registration method clearly improves detection results compared to no registration (AUC: 0.76 compared to 0.69). Moreover, feature combination using left and right breasts further improves the performance (AUC to 0.88) compared to single image features.

Original languageEnglish
Title of host publicationBreast Imaging - 12th International Workshop, IWDM 2014, Proceedings
PublisherSpringer-Verlag
Pages612-619
Number of pages8
ISBN (Print)9783319078861
DOIs
Publication statusPublished - 2014 Jan 1
Event12th International Workshop on Breast Imaging, IWDM 2014 - Gifu City, Japan
Duration: 2014 Jun 292014 Jul 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8539 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Workshop on Breast Imaging, IWDM 2014
CountryJapan
CityGifu City
Period14/6/2914/7/2

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Detecting abnormal mammographic cases in temporal studies using image registration features'. Together they form a unique fingerprint.

  • Cite this

    Martí, R., Díez, Y., Oliver, A., Tortajada, M., Zwiggelaar, R., & Lladó, X. (2014). Detecting abnormal mammographic cases in temporal studies using image registration features. In Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings (pp. 612-619). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8539 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-07887-8_85