Classification of microcalcification clusters in digital mammograms using a stack generalization based classifier

Nashid Alam, Erika R. E. Denton, Reyer Zwiggelaar

Research output: Contribution to journalArticlepeer-review

11 Citations (SciVal)
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Abstract

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (95.00 ± 0.57%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01.

Original languageEnglish
Article number76
JournalJournal of Imaging
Volume5
Issue number9
DOIs
Publication statusPublished - 12 Sept 2019

Keywords

  • digital mammogram
  • microcalcification
  • stack generalization
  • classification
  • morphological features
  • Morphological features
  • Microcalcification
  • Stack generalization
  • Classification
  • Digital mammogram

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