The discrimination system of pancreatic endoscopic ultrasonography image based on M-band wavelet transfom
10.3760/cma.j.issn.1007-5232.2010.08.008
- VernacularTitle:基于M带小波变换方法的胰腺癌超声内镜图像判别系统的建立研究
- Author:
Minmin ZHANG
;
Hua YANG
;
Zhendong JIN
;
Zheyuan CAI
;
Jianguo YU
;
Zhaoshen LI
- Publication Type:Journal Article
- Keywords:
Pancreatic cancer;
Endoscopic ultrasonography;
M-band wavelet transform;
Multifractal
- From:
Chinese Journal of Digestive Endoscopy
2010;27(8):419-422
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop and evaluate the digital discrimination system for pancreatic ultrasound endoscopy images. Methods EUS images of 153 pancreatic cancer and 63 non-cancer cases were selected. According to the multi-fractal feature vectors based on the M-band wavelet transform, we acquired the fractal features with lower dimension with the feature screening algorithm. With the optimal feature combination, cases were classified into pancreatic cancer group and non-pancreatic cancer group automatically.Then the sensitivity, specificity and accuracy of this method were calculated, and compared with those of traditional 9 dimension fractal feature vectors. Results Three kinds of multi-fractal dimensions were introduced to the framework of M-band wavelet transform according to the EUS images to form fractal vectors of 18 dimension. With the selection by sequence forward search (SFS) algorithm, 7 dimension of feature vectors were chosen and were combined with bi-order multi-fractal dimension to a better feature combination. The Bayes, support vector machine (SVM) and ModestAdaBoost classifiers were introduced to evaluate the classification efficiency, resulting in a classification accuracy of 97.98% and short running time of 0. 49 s with lower feature dimension. Conclusion These data suggest the feasibility, accuracy, noninvasiveness and efficacy of classification of EUS images to differentiate pancreatic cancer from normal tissue based on the Mband wavelet transform algorithm. It is a new and valuable research area in diagnosis of pancreatic cancer.
