title: Classification of low resolution virtual slides from breast tumor sections: comparison between global and local analysis.
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.
authors: Quantitative Histo-Imagery team (GRECAN), Fran ois Baclesse Cancer Center, Ave du Général Harris, Caen 14076 Cedex 5, France. m.oger@baclesse.fr
authors: Myriam
authors: M
authors: Oger
authors: Philippe
authors: P
authors: Belhomme
authors: Metin N
authors: MN
authors: Gurcan
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D016000:Cluster Analysis; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D014584:User-Computer Interface