Detection of lobular carcinoma in situ(LCIS) by image analysis

Sujin Kim, Desok Kim, Hyun Joo Choi, Hee Jae Joo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this study, we aimed to develop a quantitative image analysis method that may enhance the detection of the lobular carcinoma in-situ (LCIS) in breast cancer specimens. Glandular areas were segmented by using mathematical morphology from 5X histologic images of breast cancer cases (n=213). Computational features such as shape, intensity, and texture were extracted from glandular areas. Segmented glandular areas of LCIS were significantly larger, thicker, lower and less variant in intensity, compared to normal areas (Mann-Whitney test, p<0.01). Our models based on data mining algorithms detected LCIS frames at the accuracy rate close to 99%. Our proposed methods may be well incorporated into a further development of computer aided detection (CAD) software for the screening of whole slide images to locate the LCIS areas.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Pages623-624
Number of pages2
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 - Atlanta, GA, United States
Duration: Nov 12 2011Nov 15 2011

Publication series

Name2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

Conference

Conference2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Country/TerritoryUnited States
CityAtlanta, GA
Period11/12/1111/15/11

Keywords

  • Breast Cancer
  • Computer Aided Detection
  • Image Analysis
  • Lobular Carcinoma In-situ (LCIS)
  • Pattern Recognition
  • Whole Slide Images

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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