Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns

Shelda Sajeev, Mariusz Bajger, Gobert Lee, Chisako Muramatsu, Hiroshi Fujita

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

Abstract

The paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. This task is particularly challenging even for experienced radiologists due to lack of clear boundaries between the cancerous and normal tissue. Multi-scale analysis of structured micro-patterns generated from local binary patterns (LBP) was used to generate a very small number of features which allowed for successful detection of cancerous regions. The proposed technique was tested on two publicly available datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The area under the receiver operating characteristic (AUC) curve for DDSM with 2 features only was 0.99 and 0.92 for INbreast with 3 features.

Original languageEnglish
Title of host publication15th International Workshop on Breast Imaging, IWBI 2020
EditorsHilde Bosmans, Nicholas Marshall, Chantal Van Ongeval
Place of PublicationWashington, USA
PublisherSPIE
Number of pages6
ISBN (Electronic)9781510638327
ISBN (Print)9781510638310
DOIs
Publication statusPublished - 2020
Event15th International Workshop on Breast Imaging, IWBI 2020 - Leuven, Belgium
Duration: 25 May 202027 May 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11513
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference15th International Workshop on Breast Imaging, IWBI 2020
CountryBelgium
CityLeuven
Period25/05/2027/05/20

Keywords

  • Breast cancer
  • CAD
  • Dense ROI
  • Local binary pattern
  • Machine learning
  • Mammography
  • Structured micro-patterns

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