Classification Model of Corneal Opacity Based on Digital Image Features.
10.3969/j.issn.1671-7104.2021.04.002
- Author:
Peng LUO
1
;
Jilong ZHENG
2
;
Peng ZHOU
1
;
Yongde ZHANG
1
;
Shijie CHANG
1
;
Xianzheng SHA
1
Author Information
1. Department of Biomedical Engineering, China Medical University, Shenyang, 110122.
2. Department of Forensic Medicine, China Criminal Police University, Shenyang, 110035.
- Publication Type:Journal Article
- Keywords:
SVM;
corneal opacity;
feature extraction;
feature selection
- MeSH:
Animals;
Corneal Opacity;
Support Vector Machine;
Swine
- From:
Chinese Journal of Medical Instrumentation
2021;45(4):361-365
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:According to the digital image features of corneal opacity, a multi classification model of support vector machine (SVM) was established to explore the objective quantification method of corneal opacity.
METHODS:The cornea digital images of dead pigs were collected, part of the color features and texture features were extracted according to the previous experience, and the SVM multi classification model was established. The test results of the model were evaluated by precision, sensitivity and
RESULTS:In the classification of corneal opacity, the highest
CONCLUSIONS:The SVM multi classification model can classify the degree of corneal opacity.
