Unsupervised tissue image segmentation through object-oriented texture
Author
Tosun, Akif Burak
Sokmensuer, C.
Gündüz-Demir, Çiğdem
Date
2010Source Title
2010 20th International Conference on Pattern Recognition
Print ISSN
1051-4651
Publisher
IEEE
Pages
2516 - 2519
Language
English
Type
Conference PaperItem Usage Stats
146
views
views
107
downloads
downloads
Abstract
This paper presents a new algorithm for the unsupervised segmentation of tissue images. It relies on using the spatial information of cytological tissue components. As opposed to the previous study, it does not only use this information in defining its homogeneity measures, but it also uses it in its region growing process. This algorithm has been implemented and tested. Its visual and quantitative results are compared with the previous study. The results show that the proposed segmentation algorithm is more robust in giving better accuracies with less number of segmented regions. © 2010 IEEE.
Keywords
Image segmentationQuantitative medical image analysis
Texture analysis
Medical image analysis
Object oriented
Quantitative result
Region growing
Segmentation algorithms
Segmented regions
Spatial informations
Texture analysis
Tissue components
Tissue image segmentation
Tissue images
Unsupervised segmentation
Algorithms
Image analysis
Information use
Medical imaging
Pattern recognition
Textures
Tissue
Image segmentation
Permalink
http://hdl.handle.net/11693/28520Published Version (Please cite this version)
http://dx.doi.org/10.1109/ICPR.2010.616Collections
Related items
Showing items related by title, author, creator and subject.
-
Tissue object patterns for segmentation in histopathological images
Gündüz-Demir, Çiğdem (ACM, 2011)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 ... -
Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection
Tosun, A. B.; Kandemir, M.; Sokmensuer, C.; Gunduz Demir, C. (Elsevier BV, 2009-06)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 ... -
Quantification of SLIT-ROBO transcripts in hepatocellular carcinoma reveals two groups of genes with coordinate expression
Avci, M. E.; Konu, O.; Yagci, T. (BioMed Central, 2008)Background: SLIT-ROBO families of proteins mediate axon pathfinding and their expression is not solely confined to nervous system. Aberrant expression of SLIT-ROBO genes was repeatedly shown in a wide variety of cancers, ...