Breast Image Pre-processing For Mammographic Tissue Segmentation

Wenda He, Peter Hogg, Arne Juette, Erika R. E. Denton, Reyer Zwiggelaar

Research output: Contribution to journalArticlepeer-review

20 Citations (SciVal)
285 Downloads (Pure)

Abstract

During mammographic image acquisition, a compression paddle is used to even the breast thickness in order to obtain optimal image quality. Clinical observation has indicated that some mammograms may exhibit abrupt intensity change and low visibility of tissue structures in the breast peripheral areas. Such appearance discrepancies can affect image interpretation and may not be desirable for computer aided mammography, leading to incorrect diagnosis and/or detection which can have a negative impact on sensitivity and specificity of screening mammography. This paper describes a novel mammographic image pre-processing method to improve image quality for analysis. An image selection process is incorporated to better target problematic images. The processed images show improved mammographic appearances not only in the breast periphery but also across the mammograms. Mammographic segmentation and risk/density classification were performed to facilitate a quantitative and qualitative evaluation. When using the processed images, the results indicated more anatomically correct segmentation in tissue specific areas, and subsequently better classification accuracies were achieved. Visual assessments were conducted in a clinical environment to determine the quality of the processed images and the resultant segmentation. The developed method has shown promising results. It is expected to be useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment
Original languageEnglish
Pages (from-to)61-73
Number of pages13
JournalComputers in Biology and Medicine
Volume67
Early online date14 Oct 2015
DOIs
Publication statusPublished - 01 Dec 2015

Keywords

  • mammographic segmentation
  • risk assessment
  • density classification
  • peripheral enhancement
  • BI-RADS
  • Tabár

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