title: Texture and color based image segmentation and pathology detection in capsule endoscopy videos.
abstract: This paper presents an in-depth study of several approaches to exploratory analysis of wireless capsule endoscopy images (WCE). It is demonstrated that versatile texture and color based descriptors of image regions corresponding to various anomalies of the gastrointestinal tract allows their accurate detection of pathologies in a sequence of WCE frames. Moreover, through classification of single pixels described by texture features of their neighborhood, the images can be segmented into homogeneous areas well matched to the image content. For both, detection and segmentation tasks the same procedure is applied which consists of features calculation, relevant feature subset selection and classification stages. This general three-stage framework is realized using various recognition strategies. In particular, the performance of the developed Vector Supported Convex Hull classification algorithm is compared against Support Vector Machines run in configuration with two different feature selection methods.
authors.affiliation: Technical University of Lodz, Institute of Electronics, Medical Electronics Division, 90-924 Lodz, ul. Wolczanska 211/215, Poland. Electronic address: pms@p.lodz.pl.
authors.forename: Piotr
authors.initials: P
authors.lastname: Szczypiński
authors.forename: Artur
authors.initials: A
authors.lastname: Klepaczko
authors.forename: Marek
authors.initials: M
authors.lastname: Pazurek
authors.forename: Piotr
authors.initials: P
authors.lastname: Daniel
mesh_terms: D053704:Capsule Endoscopy; D003116:Color; D004502:Education, Medical, Continuing