Incorporating texture features in a computer-aided breast lesion diagnosis system for automated three-dimensional breast ultrasound

Haixia Liu, Tao Tan, Jan Van Zelst, Ritse Mann, Nico Karssemeijer, Bram Platel

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

We investigated the benefits of incorporating texture features into an existing computer-aided diagnosis (CAD) system for classifying benign and malignant lesions in automated three-dimensional breast ultrasound images. The existing system takes into account 11 different features, describing different lesion properties; however, it does not include texture features. In this work, we expand the system by including texture features based on local binary patterns, gray level co-occurrence matrices, and Gabor filters computed from each lesion to be diagnosed. To deal with the resulting large number of features, we proposed a combination of feature-oriented classifiers combining each group of texture features into a single likelihood, resulting in three additional features used for the final classification. The classification was performed using support vector machine classifiers, and the evaluation was done with 10-fold cross validation on a dataset containing 424 lesions (239 benign and 185 malignant lesions). We compared the classification performance of the CAD system with and without texture features. The area under the receiver operating characteristic curve increased from 0.90 to 0.91 after adding texture features (p<0.001).

Original languageEnglish
Article number024501
JournalJournal of Medical Imaging
Volume1
Issue number2
DOIs
Publication statusPublished - 1 Jul 2014
Externally publishedYes

Keywords

  • Gabor filters
  • computer-aided diagnosis
  • gray level co-occurrence matrix texture features
  • local binary patterns
  • texture features
  • three-dimensional automated breast ultrasound
  • three-dimensional texture features

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