Automatic segmentation of colon glands using object-graphs

Date
2010
Authors
Gunduz Demir, C.
Kandemir, M.
Tosun, A. B.
Sokmensuer, C.
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
Medical Image Analysis
Print ISSN
1361-8415
Electronic ISSN
Publisher
Elsevier BV
Volume
14
Issue
1
Pages
1 - 12
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
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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. © 2009 Elsevier B.V. All rights reserved.

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Book Title
Keywords
Attributed graphs, Colon adenocarcinoma, Gland segmentation, Histopathological image analysis, Image segmentation, Object - graphs
Citation
Published Version (Please cite this version)