Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

Maxine Tan, Jiantao Pu, Bin Zheng

Research output: Contribution to journalArticleResearchpeer-review

27 Citations (Scopus)

Abstract

The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were given to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC = 0.793 ± 0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.

Original languageEnglish
Pages (from-to)4357-4373
Number of pages17
JournalPhysics in Medicine & Biology
Volume59
Issue number15
DOIs
Publication statusPublished - 7 Aug 2014
Externally publishedYes

Keywords

  • breast cancer screening
  • computer-aided diagnosis (CAD)
  • full-field digital mammography (FFDM)
  • quantitative mammographic density measures

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