Ciaccio Edward J, Tennyson Christina A, Bhagat Govind, Lewis Suzanne K, Green Peter H
Department of Medicine-Celiac Disease Center, Columbia University, New York, USA.
Department of Medicine-Celiac Disease Center, Columbia University, New York, USA Department of Pathology and Cell Biology, Columbia University, New York, USA.
Biomed Mater Eng. 2014;24(6):1913-23. doi: 10.3233/BME-141000.
Celiac disease commonly occurs in approximately 1% of populations, but it can be difficult to diagnose. The standard method to diagnose celiac disease includes analysis of endoscopy images of the small intestinal mucosa to detect presence of villous atrophy, which can be subtle. We have devised a means to improve the image-based detection of villous atrophy and other abnormality in videocapsule endoscopy by means of incorporating basis images. Basis images were extracted from a series of 200 consecutive image frames acquired over 100 seconds at the level of the duodenal bulb in 13 celiac patients and in 13 controls. They were converted from color to 256 grayscale levels (gsl; 0 = black, 255 = white). Eight basis images were used for analysis. A histogram was constructed for each basis image, and the mean and standard deviation of the histogram values were tabulated. The significance of the difference in histogram mean level for celiacs versus controls was determined. Then the histogram mean was plotted versus the standard deviation, separately for all eight basis images, and also averaged for all bases combined. The mean histogram level for celiacs was 127.59+6.05 gsl versus 129.25+5.53 gsl for controls (p< 0.05). Thus celiac basis images tended to be darker and also more variable as compared with controls. For nonlinear classification, using the average of combined basis images, the sensitivity was 84.6% while the specificity was 92.3%. Using the single most important basis image for nonlinear classification, the sensitivity was 84.6% while the specificity was 76.9%. Construction of basis images can be useful to condense videocapsule image series into salient information, for detection of differences in grayscale level mean and variation in celiac versus control image series, and for classification of celiac versus control videoclips with nonlinear discriminant functions.
乳糜泻在人群中的发病率通常约为1%，但可能难以诊断。诊断乳糜泻的标准方法包括分析小肠黏膜的内镜图像以检测绒毛萎缩的存在，而这种萎缩可能很细微。我们设计了一种方法，通过合并基础图像来改进视频胶囊内镜中基于图像的绒毛萎缩和其他异常的检测。基础图像是从13名乳糜泻患者和13名对照者十二指肠球部水平在100秒内连续获取的200个图像帧系列中提取的。它们从彩色转换为256级灰度（gsl；0=黑色，255=白色）。使用8个基础图像进行分析。为每个基础图像构建直方图，并将直方图值的均值和标准差制成表格。确定乳糜泻患者与对照者直方图平均水平差异的显著性。然后分别针对所有8个基础图像绘制直方图均值与标准差的关系图，并对所有基础图像进行合并平均。乳糜泻患者的直方图平均水平为127.59+6.05 gsl，而对照者为129.25+5.53 gsl（p<0.05）。因此，与对照者相比，乳糜泻基础图像往往更暗且变化更大。对于非线性分类，使用合并基础图像的平均值时，灵敏度为84.6%，特异性为92.3%。使用单个最重要的基础图像进行非线性分类时，灵敏度为84.6%，特异性为76.9%。基础图像的构建有助于将视频胶囊图像系列浓缩为显著信息，用于检测乳糜泻与对照图像系列中灰度水平均值的差异和变化，以及使用非线性判别函数对乳糜泻与对照视频片段进行分类。
