Differential diagnosis of early osteoarthritis based on in-line phase contrast imaging and support vector machine

doi:10.3969/j.issn.1006-7795.2022.01.020

Journal of Capital Medical University ›› 2022, Vol. 43 ›› Issue (1): 120-126.doi: 10.3969/j.issn.1006-7795.2022.01.020

• Clinical Research • Previous Articles Next Articles

Differential diagnosis of early osteoarthritis based on in-line phase contrast imaging and support vector machine

Li Jun^1△, Cheng Cheng^1△, Dong Linan², Wu Mingshu³, Zhang Lu^3*

1. Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China;
2. Department of Imaging, The General Hospital of the People's Liberation Army, Beijing 100039, China;
3. School of Biomedical Engineering, Capital Medical University, Beijing 100069, China

Received:2021-07-06 Online:2022-02-21 Published:2022-01-27
Contact: * E-mail:luzhang1210@ccmu.edu.cn
Supported by:
National Natural Science Foundation of China (81401549).

Abstract

Abstract: Objective To establish the classification model for normal and early osteoarthritis (OA) of cartilage tissue by in-line phase contrast imaging (IL-PCI) technique and support vector machine (SVM) algorithm. Methods The study samples were in vitro knee cartilage samples from patients undergoing arthroplasty. The research subjects were divided into normal group and early OA group, 18 cases in each group. Computed tomography (CT) images of cartilage tissues were obtained by in-line phase contrast imaging (IL-PCI) technology. Totally 26 texture parameters were extracted from the two groups of CT images by using three analysis methods of stroke length matrix, fractal dimension and discrete wavelet transform. Dimensionality of texture parameters was reduced by principal component analysis. A SVM classification model was established based on texture parameters to classify normal cartilage and early OA cartilage automatically. Results The changes of microstructure in normal and early OA cartilage could be clearly observed by IL-PCI technique. The cartilage texture parameters of the two groups were compared, and the results showed that most of the texture parameters were significantly different. The classification accuracy of the two groups can reach 86.1% by support vector machine algorithm. Conclusion The support vector machine algorithm and IL-PCI method can provide an effective auxiliary method for the diagnosis of early osteoarthritis.

Key words: in-line phase contrast imaging (IL-PCI), support vector machine, osteoarthritis, texture feature

CLC Number:

R684.3

Li Jun, Cheng Cheng, Dong Linan, Wu Mingshu, Zhang Lu. Differential diagnosis of early osteoarthritis based on in-line phase contrast imaging and support vector machine[J]. Journal of Capital Medical University, 2022, 43(1): 120-126.

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Differential diagnosis of early osteoarthritis based on in-line phase contrast imaging and support vector machine

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