Quantification of virtual slides: Approaches to analysis of content-based image information

• Published in: Journal of pathology informatics Vol. 2; no. 1; p. 2
• Main Author: Kayser, Klaus
• Format: Journal Article
• Language: English
• Published: India Elsevier Inc 01.01.2011; Medknow Publications and Media Pvt. Ltd; Medknow Publications & Media Pvt Ltd; Elsevier
• Subjects: Cancer; Care and treatment; Content-based image information; Health aspects; image standardization; Methods; Microscope and microscopy; object; Pathologists; Practice; predictive diagnosis; Review; structure; texture; India; Content-based image information; predictive diagnosis; image standardization; texture; structure; object
• ISSN: 2153-3539; 2229-5089; 2153-3539
• DOI: 10.4103/2153-3539.74945

Virtual microscopy, which is the diagnostic work on completely digitized histological and cytological slides as well as blood smears, is at the stage to be implemented in routine diagnostic surgical pathology (tissue-based diagnosis) in the near future, once it has been accepted by the US Food and Drug Administration. The principle of content-based image information, its mandatory prerequisites to obtain reproducible and stable image information as well as the different compartments that contribute to image information are described in detail. Automated extraction of content-based image information requires shading correction, constant maximum of grey values, and standardized grey value histograms. The different compartments to evaluate image information include objects, structure, and texture. Identification of objects and derived structure depend on segmentation accuracy and applied procedures; textures contain pixel-based image information only. All together, these image compartments posses the discrimination power to distinguish between object space and background, and, in addition, to reproducibly define regions of interest (ROIs). ROIs are image areas which display the information that is of preferable interest to the viewing pathologist. They contribute to the derived diagnosis to a higher level when compared with other image areas. The implementation of content-based image information algorithms to be applied for predictive tissue-based diagnoses is described in detail.