Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram

Shelda Sajeev, Mariusz Bajger, Gobert Lee

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

Abstract

Identifying breast tumor in a mammogram is a challenging task even for experienced radiologists if the tumor is located in a dense tissue. In this study, a novel superpixel based graph modeling technique is proposed to extract texture features from the computer identified suspicious regions of mammograms. Graph models are constructed from specific structured superpixel patterns and used to generate feature vectors used for classifications of regions of mammograms. Two mammographic datasets were used to evaluate the effectiveness of the proposed approach: the publicly available Digital Database for Screening Mammography (DDSM), and a local database of mammograms (BSSA). Using Linear Discriminant Analysis (LDA) classifier, an AUC score of 0.910 was achieved for DDSM and 0.893 for BSSA. The results indicate that graph models can capture texture features capable of identifying masses located in dense tissues, and help improve computer-aided detection systems.

Original languageEnglish
Title of host publicationGraph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings
EditorsDaoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu
PublisherSpringer Gabler
Pages147-154
Number of pages8
ISBN (Print)9783030358167
DOIs
Publication statusPublished - 1 Jan 2019
Externally publishedYes
Event1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 17 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11849 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period17/10/1917/10/19

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Keywords

  • Dense background
  • Graph modeling
  • Mammography
  • Mass localization
  • Superpixel tessellation

Cite this

Sajeev, S., Bajger, M., & Lee, G. (2019). Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram. In D. Zhang, L. Zhou, B. Jie, & M. Liu (Eds.), Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 147-154). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11849 LNCS). Springer Gabler. https://doi.org/10.1007/978-3-030-35817-4_18