title: Automatic segmentation of colon glands using object-graphs.
abstract: Gland segmentation is an important step to automate the analysis of biopsies that contain glandular structures. However, this remains a challenging problem as the variation in staining, fixation, and sectioning procedures lead to a considerable amount of artifacts and variances in tissue sections, which may result in huge variances in gland appearances. In this work, we report a new approach for gland segmentation. This approach decomposes the tissue image into a set of primitive objects and segments glands making use of the organizational properties of these objects, which are quantified with the definition of object-graphs. As opposed to the previous literature, the proposed approach employs the object-based information for the gland segmentation problem, instead of using the pixel-based information alone. Working with the images of colon tissues, our experiments demonstrate that the proposed object-graph approach yields high segmentation accuracies for the training and test sets and significantly improves the segmentation performance of its pixel-based counterparts. The experiments also show that the object-based structure of the proposed approach provides more tolerance to artifacts and variances in tissues.
authors.affiliation: Department of Computer Engineering, Bilkent University, Ankara TR-06800, Turkey. gunduz@cs.bilkent.edu.tr
authors.forename: Cigdem
authors.initials: C
authors.lastname: Gunduz-Demir
authors.forename: Melih
authors.initials: M
authors.lastname: Kandemir
authors.forename: Akif Burak
authors.initials: AB
authors.lastname: Tosun
authors.forename: Cenk
authors.initials: C
authors.lastname: Sokmensuer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003106:Colon / Q000166:cytology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity