Automatic segmentation of colon glands using object-graphs.

Cigdem Gunduz Demir, Melih Kandemir, Akif Burak Tosun, Cenk Sokmensuer

Research output: Contribution to journalJournal articleResearchpeer-review


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.
Original languageEnglish
JournalMedical Image Analysis
Issue number1
Pages (from-to)1-12
Publication statusPublished - Feb 2010
Externally publishedYes


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