Tissue object patterns for segmentation in histopathological images
Author
Gündüz-Demir, Çiğdem
Date
2011Source Title
ISABEL '11 Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Publisher
ACM
Language
English
Type
Conference PaperItem Usage Stats
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Abstract
In the current practice of medicine, histopathological examination is the gold standard for routine clinical diagnosis and grading of cancer. However, as this examination involves the visual analysis of biopsies, it is subject to a considerable amount of observer variability. In order to decrease the variability, it has been proposed to develop systems that mathematically model the histopathological tissue images and automate the analysis. Segmentation constitutes the first step for most of these automated systems. Nevertheless, the segmentation in histopathological images remains a challenging task since these images typically show variances due to their complex nature and may include a large amount of noise and artifacts due to the tissue preparation procedures. In our research group, we recently developed different segmentation algorithms that rely on representing a tissue image with a set of tissue objects and using the structural pattern of these objects in segmentation. In this paper, we review these segmentation algorithms, discussing their clinical demonstrations on colon tissues. © 2011 ACM.
Keywords
Gland segmentationAutomated systems
Clinical diagnosis
Colon tissues
Complex nature
Gold standards
Histopathological examinations
Histopathological images
Object patterns
Observer variability
Research groups
Segmentation algorithms
Structural pattern
Tissue images
Tissue preparation
Visual analysis
Algorithms
Automation
Communication
Image texture
Textures
Tissue
Image segmentation
Permalink
http://hdl.handle.net/11693/28290Published Version (Please cite this version)
http://dx.doi.org/10.1145/2093698.2093853Collections
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