Automatic segmentation of colon glands using object-graphs


GÜNDÜZ DEMİR Ç., Kandemir M., Tosun A. B. , SÖKMENSÜER C.

MEDICAL IMAGE ANALYSIS, vol.14, no.1, pp.1-12, 2010 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 1
  • Publication Date: 2010
  • Doi Number: 10.1016/j.media.2009.09.001
  • Journal Name: MEDICAL IMAGE ANALYSIS
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.1-12

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. (C) 2009 Elsevier B. V. All rights reserved.