Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection
Date
2009-06Source Title
Pattern Recognition
Print ISSN
0031-3203
Publisher
Elsevier BV
Volume
42
Issue
6
Pages
1104 - 1112
Language
English
Type
ArticleItem Usage Stats
128
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views
144
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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.
Keywords
Cancer detectionColon 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
Permalink
http://hdl.handle.net/11693/22733Published Version (Please cite this version)
http://dx.doi.org/10.1016/j.patcog.2008.07.007Collections
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