title: Use of basis images for detection and classification of celiac disease.
abstract: 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.
authors.affiliation: Department of Medicine-Celiac Disease Center, Columbia University, New York, USA.
authors.forename: Edward J
authors.initials: EJ
authors.lastname: Ciaccio
authors.affiliation: Department of Medicine-Celiac Disease Center, Columbia University, New York, USA.
authors.forename: Christina A
authors.initials: CA
authors.lastname: Tennyson
authors.affiliation: Department of Medicine-Celiac Disease Center, Columbia University, New York, USA Department of Pathology and Cell Biology, Columbia University, New York, USA.
authors.forename: Govind
authors.initials: G
authors.lastname: Bhagat
authors.affiliation: Department of Medicine-Celiac Disease Center, Columbia University, New York, USA.
authors.forename: Suzanne K
authors.initials: SK
authors.lastname: Lewis
authors.affiliation: Department of Medicine-Celiac Disease Center, Columbia University, New York, USA.
authors.forename: Peter H
authors.initials: PH
authors.lastname: Green
mesh_terms: D000465:Algorithms; D053704:Capsule Endoscopy / Q000379:methods*; D002446:Celiac Disease / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Basis images; celiac disease; classification; histogram; nonlinear discriminant function