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Proceedings Paper

Analysis of breast lesions on contrast-enhanced magnetic resonance images using high-dimensional texture features
Author(s): Mahesh B. Nagarajan; Markus B. Huber; Thomas Schlossbauer; Gerda Leinsinger; Axel Wismueller
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Paper Abstract

Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were used to classify the character of suspicious breast lesions as benign or malignant on dynamic contrast-enhanced MRI studies. Lesions were identified and annotated by an experienced radiologist on 54 MRI exams of female patients where histopathological reports were available prior to this investigation. GLCMs were then extracted from these 2D regions of interest (ROI) for four principal directions (0°, 45°, 90° & 135°) and used to compute Haralick texture features. A fuzzy k-nearest neighbor (k- NN) classifier was optimized in ten-fold cross-validation for each texture feature and the classification performance was calculated on an independent test set as a function of area under the ROC curve. The lesion ROIs were characterized by texture feature vectors containing the Haralick feature values computed from each directional-GLCM; and the classifier results obtained were compared to a previously used approach where the directional-GLCMs were summed to a nondirectional GLCM which could further yield a set of texture feature values. The impact of varying the inter-pixel distance while generating the GLCMs on the classifier's performance was also investigated. Classifier's AUC was found to significantly increase when the high-dimensional texture feature vector approach was pursued, and when features derived from GLCMs generated using different inter-pixel distances were incorporated into the classification task. These results indicate that lesion character classification accuracy could be improved by retaining the texture features derived from the different directional GLCMs rather than combining these to yield a set of scalar feature values instead.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241G (9 March 2010); doi: 10.1117/12.844552
Show Author Affiliations
Mahesh B. Nagarajan, Univ. of Rochester Medical Ctr. (United States)
Markus B. Huber, Univ. of Rochester Medical Ctr. (United States)
Thomas Schlossbauer, Ludwig-Maximilians-Univ. München (Germany)
Gerda Leinsinger, Ludwig-Maximilians-Univ. München (Germany)
Axel Wismueller, Univ. of Rochester Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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