DocumentCode
2411231
Title
Classification of low resolution virtual slides from breast tumor sections: Comparison between global and local analysis
Author
Oger, M. ; Belhomme, P. ; Gurcan, M.N.
Author_Institution
Quantitative Histo-Imagery team (GRECAN), Francois Baclesse Cancer Center, Caen, France
fYear
2009
fDate
3-6 Sept. 2009
Firstpage
6671
Lastpage
6674
Abstract
In this paper, a comparison is made between a global and a local approach for computer-aided classification of virtual slides from breast tumor sections. The first approach classifies the images according to both color and texture information from the whole tissue, whereas the second one classifies them only on shape and texture from epithelial compartment. The originality of this study is that only low resolution virtual slides are used. As the cytological details are not visible at low resolution, the classification is only based on architectural aspect of each lesion. Experimental results on images from breast tumor sections show that some information is already present in low resolution virtual slides, allowing the classification in benign lesions versus malignant carcinomas.
Keywords
biological organs; biomedical optical imaging; cancer; image classification; image colour analysis; image texture; medical image processing; tumours; benign lesions; breast tumor sections; computer-aided classification; global analysis; image classification; image color; image texture; local analysis; low resolution virtual slides; malignant carcinomas; Breast cancer; Breast tumors; Image analysis; Image color analysis; Image texture analysis; Information analysis; Lesions; Microscopy; Pathology; Shape; Breast Neoplasms; Cluster Analysis; Diagnosis, Differential; Female; Humans; User-Computer Interface;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Conference_Location
Minneapolis, MN
ISSN
1557-170X
Print_ISBN
978-1-4244-3296-7
Type
conf
DOI
10.1109/IEMBS.2009.5334513
Filename
5334513
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