Objective To process the image of endoscopic uhrasonography(EUS)by digital imaging processing(DIP)and pattem recognition,and to evaluate its efficacy in diagnosis of pancreatic adenocarcinoma.Methods Two hundreds and sixteen patients,who underwent EUS between Feb 2005 and Feb 2007,were randomly recruited to the study.The cohort jncluded 153 cases of pancreatic cancer,which were confirmed by cytological findings after fine-needle aspiration,and 63 cases of non-pancreatic cancer(normal pancreas and chronic panereatitis).The texture features of the EUS image were selected and extracted,and cases were automatically divided into cancer and non-cancer based on findings of support vector machine (SVM).Sensitivity,specificity and accuracy of the technique were calculated.Results From each region of interest(ROI),a total of69 texture features vest in 9 sets were extracted,and 25 features with most set interval were taken as initial.The images of 216 cases were divided randomly into training set(108 eases,76 cancer and 32 non cancer)and testing set(108 cases,77 cancer and 31 non cancer).After 50 times of random tests,the average accuracy,sensitivity and specificity of the diagnosis of pancreatic cancer were (97.98±1.237)%,(94.32±0.0354)%,and(99.45±0.0102)%respectively.Conclusion DIP,combined with computer aided EUS imaging,is an accurate and noninvasive technique in diagnosis of pancreatic cancer.which warrants novel and further researches.

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.