Unsupervised tissue image segmentation through object-oriented texture
Date
2010
Editor(s)
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
2010 20th International Conference on Pattern Recognition
Print ISSN
1051-4651
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
2516 - 2519
Language
English
Type
Journal Title
Journal ISSN
Volume Title
Series
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.
Course
Other identifiers
Book Title
Keywords
Image segmentation , Quantitative 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