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Morphological Area Gradient: System-independent Dense Tissue Segmentation in Mammography Images

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

Details

Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
Pages4855-4858
Number of pages4
ISBN (Electronic)978-1-5386-1311-5
ISBN (Print)978-1-5386-1312-2
DOIs
Publication statusPublished - 7 Oct 2019
Publication typeA4 Article in a conference publication
EventAnnual International Conference of the IEEE Engineering in Medicine and Biology Society -
Duration: 1 Jan 1900 → …

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
ISSN (Print)1557-170X
ISSN (Electronic)1558-4615

Conference

ConferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
Period1/01/00 → …

Abstract

Breast density has been identified as one of the strongest risk factors for breast cancer. However, the development of reliable and reproducible methods for the automatic dense tissue segmentation has been an important challenge. Due to the complexity of the acquisition process of mammography images, current approaches need to be calibrated for specific mammographic systems or require access to raw mammograms. In this work, we introduce the Morphological Area Gradient (MAG) as a generic measure for mammography images. MAG is generic in the sense that it does not need calibration or access to raw mammograms. At the core of MAG is the derivative of the area of segmented tissue with respect to the pixel intensity. We have found that the high-density regions can be automatically segmented by minimizing the MAG of a mammogram. To verify the performance of MAG, we collected 566 full-field digital mammograms using two different medical devices and a human expert manually annotated the high-density regions in each image. The proposed MAG method yields a median absolute error of 7.6% and a Dices similarity coefficient of 0.83, which are superior to other clinically validated state-of-the-art algorithms.

Keywords

  • Mammography, Estimation, Calibration, Image segmentation, Breast cancer

Publication forum classification

Field of science, Statistics Finland