Name
Abstract | ||
|---|---|---|
CT imaging shows that it is approximately symmetrical about the perpendicular bisector. Based on this medical knowledge guidance, symmetry theory based classification algorithm in CT image database is presented in this paper. First of all, the definitions of the weak symmetry and strong symmetry were given. Then, the weak symmetry was applied to the first stage classification of the CT images. Secondly, we proposed the combination of weak symmetry and strong symmetry for the second stage classification. Finally, according to the tumor edge profile, tumors are divided into benign and malignant lesions by extracting some features of the tumor in the third stage classification. In this paper, sample size requirements of SVM (Support Vector Machine) classifier were selected to classify the CT images. Experimental results show that symmetry theory based classification algorithm in CT image database can increase the accuracy of the classification and reduce the time of the doctor's diagnosis. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICNC.2014.6975921 | ICNC |
Keywords | Field | DocType |
computerised tomography,feature extraction,image classification,medical image processing,support vector machines,tumours,visual databases,CT image database,SVM classifier,benign,feature extraction,malignant lesions,medical knowledge guidance,perpendicular bisector,strong symmetry,support vector machine,symmetry theory based classification algorithm,tumor edge profile,weak symmetry,CT image,multi-stage classification,strong symmetry,weak symmetry | Computer vision,Pattern recognition,Computer science,Artificial intelligence,Image database,Machine learning | Conference |
ISSN | Citations | PageRank |
2469-8814 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing-Shi Rong | 1 | 0 | 0.34 |
| Haiwei Pan | 2 | 52 | 21.31 |
| Linlin Gao | 3 | 8 | 3.87 |
| Qi-Long Han | 4 | 0 | 0.34 |
| Xiaoning Feng | 5 | 19 | 4.78 |