Ensemble discretewavelet transform and gray-level co-occurrence matrix for microcalcification cluster classification in digital mammography

Annarita Fanizzi, Teresa Maria Basile, Liliana Losurdo, Roberto Bellotti, Ubaldo Bottigli, Francesco Campobasso, Vittorio Didonna, Alfonso Fausto, Raffaella Massafra, Alberto Tagliafico, Pasquale Tamborra, Sabina Tangaro, Vito Lorusso, Daniele La Forgia

Research output: Contribution to journalArticlepeer-review

Abstract

The presence of clusters of microcalcifications is a primary sign of breast cancer. Their identification is still difficult today for radiologists, and the wrong evaluations involve unnecessary biopsies. In this paper, an automatic tool for characterizing and discriminating clusters of microcalcifications into benign/malignant in digital mammograms is proposed. A set of 104 digital mammograms including microcalcification clusters was randomly extracted from a public available database and manually labeled by our radiologists, obtaining 96 abnormal ROIs. For each so-identified ROI, a multi-scale image decomposition based on the Haar wavelet transform was performed. On the decomposition, a textural features extraction step was carried out both on each sub-image and on the corresponding gray-level co-occurrence matrix. Then, a random forest classifier was employed for classifying microcalcification clusters into benign and malignant. The study found that the most discriminant features extracted from the ROIs decomposition by Haar transform were variance and relative smoothness, whereas as regards the textural features calculated on the GLCMs corresponding to the Haar-decomposed ROI, it emerged that the relationship between the pixels of the sub-image in the diagonal direction had high discriminating power for the classification of microcalcification clusters into benign and malignant. The proposed method was evaluated in cross-validation and performed highly in the prediction of the benign/malignant ROIs, with a mean AUC value of 97.39 - 0.01%.

Original languageEnglish
Article number5388
JournalApplied Sciences (Switzerland)
Volume9
Issue number24
DOIs
Publication statusPublished - Dec 1 2019

Keywords

  • Computer-Aided Diagnosis (CADx)
  • Digital mammograms
  • Gray-level co-occurrence matrix
  • Haar wavelet transform
  • Microcalcification clusters
  • Random forest

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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