title: Micro-structural tissue analysis for automatic histopathological image annotation.
abstract: This article presents a new approach for extracting high level semantic concepts from digital histopathological images. This strategy provides not only annotation of several biological concepts, but also a coarse location of these concepts. The proposed approach is composed of five main steps: (1) a stain decomposition stage, which separates the contribution of hematoxylin and eosin dyes, (2) a color standardization that corrects color image differences, (3) a part-based representation, which describes the image in terms of the conditional probability of relevant local patches, selected by their stain contributions, (4) a discriminative classification model, which bridges out the found patterns and the biological concepts, (5) a block-based annotation strategy that identifies the multiple biological concepts within an image. A set of 655 skin images, containing 10 biological concepts of skin tissues were used for assessing the proposed approach, obtaining a sensitivity of 84% and a specificity of 67% when annotating images with multiple concepts.
authors: Bioingenium Research Group, Faculty of Medicine, National University of Colombia, Bogotá, Colombia.
authors: Gloria
authors: G
authors: Díaz
authors: Eduardo
authors: E
authors: Romero
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D012867:Skin / Q000473:pathology*; D012871:Skin Diseases / Q000145:classification* / Q000473:pathology