Automatic segmentation of colon glands using object-graphs

Date

2010

Authors

Gunduz Demir, C.
Kandemir, M.
Tosun, A. B.
Sokmensuer, C.

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Source Title

Medical Image Analysis

Print ISSN

1361-8415

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Publisher

Elsevier BV

Volume

14

Issue

1

Pages

1 - 12

Language

English

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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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Published Version (Please cite this version)