Tissue object patterns for segmentation in histopathological images

Date
2011
Advisor
Instructor
Source Title
ISABEL '11 Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Print ISSN
Electronic ISSN
Publisher
ACM
Volume
Issue
Pages
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
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.

Course
Other identifiers
Book Title
Keywords
Gland segmentation, Automated 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
Citation
Published Version (Please cite this version)