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Proceedings Paper

Local binary pattern texture-based classification of solid masses in ultrasound breast images
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Paper Abstract

Breast cancer is one of the leading causes of cancer mortality among women. Ultrasound examination can be used to assess breast masses, complementarily to mammography. Ultrasound images reveal tissue information in its echoic patterns. Therefore, pattern recognition techniques can facilitate classification of lesions and thereby reduce the number of unnecessary biopsies. Our hypothesis was that image texture features on the boundary of a lesion and its vicinity can be used to classify masses. We have used intensity-independent and rotation-invariant texture features, known as Local Binary Patterns (LBP). The classifier selected was K-nearest neighbors. Our breast ultrasound image database consisted of 100 patient images (50 benign and 50 malignant cases). The determination of whether the mass was benign or malignant was done through biopsy and pathology assessment. The training set consisted of sixty images, randomly chosen from the database of 100 patients. The testing set consisted of forty images to be classified. The results with a multi-fold cross validation of 100 iterations produced a robust evaluation. The highest performance was observed for feature LBP with 24 symmetrically distributed neighbors over a circle of radius 3 (LBP24,3) with an accuracy rate of 81.0%. We also investigated an approach with a score of malignancy assigned to the images in the test set. This approach provided an ROC curve with Az of 0.803. The analysis of texture features over the boundary of solid masses showed promise for malignancy classification in ultrasound breast images.

Paper Details

Date Published: 25 February 2012
PDF: 8 pages
Proc. SPIE 8320, Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy, 83201H (25 February 2012); doi: 10.1117/12.911653
Show Author Affiliations
Monica M. S. Matsumoto, Perelman School of Medicine, The Univ. of Pennsylvania (United States)
Chandra M. Sehgal, Perelman School of Medicine, The Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Perelman School of Medicine, The Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 8320:
Medical Imaging 2012: Ultrasonic Imaging, Tomography, and Therapy
Johan G. Bosch; Marvin M. Doyley, Editor(s)

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