title: Scale invariant texture descriptors for classifying celiac disease.
abstract: Scale invariant texture recognition methods are applied for the computer assisted diagnosis of celiac disease. In particular, emphasis is given to techniques enhancing the scale invariance of multi-scale and multi-orientation wavelet transforms and methods based on fractal analysis. After fine-tuning to specific properties of our celiac disease imagery database, which consists of endoscopic images of the duodenum, some scale invariant (and often even viewpoint invariant) methods provide classification results improving the current state of the art. However, not each of the investigated scale invariant methods is applicable successfully to our dataset. Therefore, the scale invariance of the employed approaches is explicitly assessed and it is found that many of the analyzed methods are not as scale invariant as they theoretically should be. Results imply that scale invariance is not a key-feature required for successful classification of our celiac disease dataset.
authors.affiliation: University of Salzburg, Department of Computer Sciences, Salzburg, Austria. shegen@cosy.sbg.ac.at
authors.forename: Sebastian
authors.initials: S
authors.lastname: Hegenbart
authors.forename: Andreas
authors.initials: A
authors.lastname: Uhl
authors.forename: Andreas
authors.initials: A
authors.lastname: Vécsei
authors.forename: Georg
authors.initials: G
authors.lastname: Wimmer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002446:Celiac Disease / Q000473:pathology*; D004386:Duodenum / Q000473:pathology*; D016099:Endoscopy, Gastrointestinal / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity