Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection

Date
2009-06
Authors
Tosun, A. B.
Kandemir, M.
Sokmensuer, C.
Gunduz Demir, C.
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
Pattern Recognition
Print ISSN
0031-3203
Electronic ISSN
Publisher
Elsevier BV
Volume
42
Issue
6
Pages
1104 - 1112
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Series
Abstract

Staining methods routinely used in pathology lead to similar color distributions in the biologically different regions of histopathological images. This causes problems in image segmentation for the quantitative analysis and detection of cancer. To overcome this problem, unlike previous methods that use pixel distributions, we propose a new homogeneity measure based on the distribution of the objects that we define to represent tissue components. Using this measure, we demonstrate a new object-oriented segmentation algorithm. Working with colon biopsy images, we show that this algorithm segments the cancerous and normal regions with 94.89 percent accuracy on the average and significantly improves the segmentation accuracy compared to its pixel-based counterpart. © 2008 Elsevier Ltd. All rights reserved.

Course
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Book Title
Keywords
Cancer detection, Colon adenocarcinoma, Histopathological image analysis, Image segmentation, Biopsy, Digital image storage, Image analysis, Image processing, Pixels, Textures, Color distributions, Object-oriented segmentations, Pixel distributions, Quantitative analysis, Segmentation accuracies, Staining methods, Tissue components, Unsupervised segmentations
Citation
Published Version (Please cite this version)