Image segmentation feature selection and pattern classification for mammographic microcalcifications

J. C. Fu, S. K. Lee, S. T.C. Wong, J. Y. Yeh, A. H. Wang, H. K. Wu

Research output: Contribution to journalArticlepeer-review

99 Scopus citations

Abstract

Since microcalcifications in X-ray mammograms are the primary indicator of breast cancer, detection of microcalcifications is central to the development of an effective diagnostic system. This paper proposes a two-stage detection procedure. In the first stage, a data driven, closed form mathematical model is used to calculate the location and shape of suspected microcalcifications. When tested on the Nijmegen University Hospital (Netherlands) database, data analysis shows that the proposed model can effectively detect the occurrence of microcalcifications. The proposed mathematical model not only eliminates the need for system training, but also provides information on the borders of suspected microcalcifications for further feature extraction. In the second stage, 61 features are extracted for each suspected microcalcification, representing texture, the spatial domain and the spectral domain. From these features, a sequential forward search (SFS) algorithm selects the classification input vector, which consists of features sensitive only to microcalcifications. Two types of classifiers - a general regression neural network (GRNN) and a support vector machine (SVM) - are applied, and their classification performance is compared using the Az value of the Receiver Operating Characteristic curve. For all 61 features used as input vectors, the test data set yielded Az values of 97.01% for the SVM and 96.00% for the GRNN. With input features selected by SFS, the corresponding Az values were 98.00% for the SVM and 97.80% for the GRNN. The SVM outperformed the GRNN, whether or not the input vectors first underwent SFS feature selection. In both cases, feature selection dramatically reduced the dimension of the input vectors (82% for the SVM and 59% for the GRNN). Moreover, SFS feature selection improved the classification performance, increasing the Az value from 97.01 to 98.00% for the SVM and from 96.00 to 97.80% for the GRNN.

Original languageEnglish (US)
Pages (from-to)419-429
Number of pages11
JournalComputerized Medical Imaging and Graphics
Volume29
Issue number6
DOIs
StatePublished - Sep 2005

Keywords

  • Feature selection
  • General regression neural network
  • Mammogram
  • Microcalcification
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science Applications
  • Radiology Nuclear Medicine and imaging

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