SPECIAL SECTION ON BIOMEDICAL IMAGE REPRESENTATION

Unsupervised morphological granulometric texture segmentation of digital mammograms

[+] Author Affiliations
Sooncheol Baeg, Sinan Batman, Edward R. Dougherty, Vishnu G. Kamat, Nasser Kehtarnavaz, Seunghan Kim, Anthony Popov, Krishnamoorthy Sivakumar, Robert Shah

Texas A&M University, Computer Aided Medical Diagnostic Imaging (CAMDI) Laboratory, Department of Electrical Engineering and Texas Center for Applied Technology, College Station, Texas?77843-3128

J. Electron. Imaging. 8(1), 65-75 (Jan 01, 1999). doi:10.1117/1.482685
History: Received Aug. 3, 1998; Accepted Sept. 17, 1998
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Abstract

Segmentation via morphological granulometric features is based on fitting structuring elements into image topography from below and above. Each structuring element captures a specific texture content. This paper applies granulometric segmentation to digitized mammograms in an unsupervised framework. Granulometries based on a number of flat and nonflat structuring elements are computed, local size distributions are tabulated at each pixel, granulometric-moment features are derived from these size distributions to produce a feature vector at each pixel, the Karhunen–Loeve transform is applied for feature reduction, and Voronoi-based clustering is performed on the reduced Karhunen–Loeve feature set. Various algorithmic choices are considered, including window size and shape, number of clusters, and type of structuring elements. The algorithm is applied using only granulometric texture features, using gray-scale intensity along with the texture features, and on a compressed mammogram. Segmentation results are clinically evaluated to determine the algorithm structure that best accords to an expert radiologist’s view of a set of mammograms. © 1999 SPIE and IS&T.

© 1999 SPIE and IS&T

Citation

Sooncheol Baeg ; Sinan Batman ; Edward R. Dougherty ; Vishnu G. Kamat ; Nasser Kehtarnavaz, et al.
"Unsupervised morphological granulometric texture segmentation of digital mammograms", J. Electron. Imaging. 8(1), 65-75 (Jan 01, 1999). ; http://dx.doi.org/10.1117/1.482685


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