图像分析技术在自身免疫性胰腺炎与慢性胰腺炎超声内镜鉴别诊断中的应用  

Digital imaging processing of EUS image in differentiating autoimmune pancreatitis from chronic pancreatitis

作　　者：朱建伟[1] 王雷[1] 储怡宁 侯晓佳[1] 周银火 汪源源[2] 金震东[1] 李兆申[1] 

机构地区：[1]第二军医大学附属长海医院消化内科,上海200433 [2]上海复旦大学电子工程系 [3]浙江省舟山市73236部队卫生队

出　　处：《中华消化内镜杂志》2015年第4期225-228,共4页Chinese Journal of Digestive Endoscopy

摘　　要：目的探讨数字图像处理技术在超声内镜鉴别诊断自身免疫性胰腺炎与慢性胰腺炎中的应用价值。方法回顾2005年5月至2013年1月确诊的100例慢性胰腺炎和81例自身免疫性胰腺炎患者的内镜超声图像，选择具有病变典型表现的内镜超声图像和勾画的感兴趣区域，通过计算机提取胰腺分类系统中的9大类105维特征，采用类间距法和顺序前进法筛选纹理特征的最优特征组合，通过支撑向量机建立分类模型，使用2种不同的样本集划分方法对慢性胰腺炎和自身免疫性胰腺炎病例进行自动分类，统计分类的准确率、敏感度、特异度、阳性预测值和阴性预测值。结果最优纹理特征组合包括5大类25维特征，此时准确分类率达最大（90．08％）。181例病例采用均匀划分样本集法和留一法随机划分为训练集和测试集，共进行200次随机检验，均匀划分样本集法最终分类准确率为（86．04±3．15）％、敏感度为（83．66±6．57）％、特异度为（88．54±4．37）％、阳性预测值为（85．96±4．44）％、阴性预测值为（87．12±4．39）％。结论计算机辅助的图像分析技术具有客观性、无创性，能够提高内镜超声识别自身免疫性胰腺炎的准确率，为自身免疫性胰腺炎诊断提供了一个新的有价值的研究方向。Objective To explore the feasibility of using digital imaging processing （DIP） to extract EUS image parameters for the differential diagnosis of autoimmune pancreatitis （AIP） and chronic pancreati- tis （CP）. Methods A total of 81 patients with AIP and 100 patients with CP diagnosed from May 2005 to January 2013 were recruited to this study. A total of 105 parameters of 9 categories were extracted from the region of interest by using computer-based techniques. Then the distance between class algorithm and se- quential forward selection （SFS） algorithm were used for a better combination of features. A support vector machine （SVM） predictive model was built, trained, and validated. Results Overall, 25 parameters of 5 categories were selected as a better combination of features when the incidence of accurate category was max （90. 08% ）. A total of 181 sample sets were randomly divided into a training set and a testing set by using two different algorithms and 200 random tests were performed. The average accuracy, sensitivity, specificity, the positive and negative predictive values of AIP based on the half-and-half method were （86. 04 ± 3.15）%, （83.66±6.57）% , （88.54±4.37）% , （85.96±4.44）% and （87.12±4.39）% , respectively. Conclusion Computer-aided diagnosis of EUS images is objective and non-invasive, which can improve the accuracy in differentiating AIP from CP. This technology provides a new valuable diagnostic tool for the clinical determination of AIP.

关 键 词：自身免疫性胰腺炎 慢性胰腺炎 内镜超声检查术 图像分析 支撑向量机 

分 类 号：R576[医药卫生—消化系统]