Paper Info
Title
Multiclass vertebral fracture classification using ensemble probability SVM with multi-feature selection.
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.
Year
DOI
Venue
2019
10.1117/12.2512434
Proceedings of SPIE
Keywords
DocType
Volume
Vertebral fracture classification,infinite feature selection,multiclass PSVM,ensemble learning
Conference
10950
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
0
11
Name
Order
Citations
PageRank
Liyuan Zhang102.03
Jiashi Zhao201.35
Huamin Yang31917.29
Weili Shi400.68
yu miao547.18
Fei He600.68
Wei He703.04
Yanfang Li89913.44
Ke Zhang97521.74
Kensaku Mori101125160.28
Zhengang Jiang1141.82