Journal
PATTERN RECOGNITION
Volume 43, Issue 10, Pages 3494-3506Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2010.04.023
Keywords
Support vector machines; Feature selection; Thyroid nodule classification
Funding
- National Science Council, Taiwan [NSC 94-2213-E-303-002, NSC 96-2221-E-303-001]
- Buddhist Dalin Tzu Chi General Hospital, Chia-Yi, Taiwan
- department of pathology, Buddhist Dalin Tzu Chi General Hospital, Chia-Yi, Taiwan
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Most thyroid nodules are heterogeneous with various internal components, which confuse many radiologists and physicians with their various echo patterns in ultrasound images. Numerous textural feature extraction methods are used to characterize these patterns to reduce the misdiagnosis rate. Thyroid nodules can be classified using the corresponding textural features. In this paper, six support vector machines (SVMs) are adopted to select significant textural features and to classify the nodular lesions of a thyroid. Experiment results show that the proposed method can correctly and efficiently classify thyroid nodules. A comparison with existing methods shows that the feature-selection capability of the proposed method is similar to that of the sequential-floating-forward-selection (SFFS) method, while the execution time is about 3-37 times faster. In addition, the proposed criterion function achieves higher accuracy than those of the F-score, T-test, entropy, and Bhattacharyya distance methods. (C) 2010 Elsevier Ltd. All rights reserved.
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