Multiclass vertebral fracture classification using ensemble probability SVM with multi-feature selection

Liyuan Zhang; Jiashi Zhao; Huamin Yang; Weili Shi; Yu Miao; Fei He; Wei He; Yanfang Li; Ke Zhang; Kensaku Mori; Zhengang Jiang

doi:10.1117/12.2512434

13 March 2019 Multiclass vertebral fracture classification using ensemble probability SVM with multi-feature selection

Liyuan Zhang, Jiashi Zhao, Huamin Yang, Weili Shi, Yu Miao, Fei He, Wei He, Yanfang Li, Ke Zhang, Kensaku Mori, Zhengang Jiang

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 1095025 (2019) https://doi.org/10.1117/12.2512434
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Lumbar vertebral fracture seriously endangers the health of people, which has a higher mortality. Due to the tiny difference among various fracture features in CT images, multiple vertebral fractures classification has a great challenge for computer-aided diagnosis system. To solve this problem, this paper proposes a multiclass PSVM ensemble method with multi-feature selection to recognize lumbar vertebral fractures from spine CT images. In the proposed method, firstly, the active contour model is utilized to segment lumbar vertebral bodies. It is helpful for the subsequent feature extraction. Secondly, different image features are extracted, including 3 geometric shape features, 3 texture features, and 5 height ratios. The importance of these features is analyzed and ranked by using infinite feature selection method, thus selecting different feature subsets. Finally, three multiclass probability SVMs with binary tree structure are trained on three datasets. The weighted voting strategy is used for the final decision fusion. To validate the effectiveness of the proposed method, probability SVM, K-nearest neighbor, and decision tree as base classifiers are compared with or without feature selection. Experimental results on 25 spine CT volumes demonstrate that the advantage of the proposed method compared to other classifiers, both in terms of the classification accuracy and Cohen’s kappa coefficient.

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Liyuan Zhang, Jiashi Zhao, Huamin Yang, Weili Shi, Yu Miao, Fei He, Wei He, Yanfang Li, Ke Zhang, Kensaku Mori, and Zhengang Jiang "Multiclass vertebral fracture classification using ensemble probability SVM with multi-feature selection", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095025 (13 March 2019); https://doi.org/10.1117/12.2512434

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KEYWORDS

Feature selection

Computed tomography

Feature extraction

Image segmentation

Spine

Image classification

Computer aided diagnosis and therapy

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