Paper Info
Title
Automated classification of glandular tissue by statistical proximity sampling
Abstract
AbstractDue to the complexity of biological tissue and variations in staining procedures, features that are based on the explicit extraction of properties from subglandular structures in tissue images may have difficulty generalizing well over an unrestricted set of images and staining variations. We circumvent this problem by an implicit representation that is both robust and highly descriptive, especially when combined with a multiple instance learning approach to image classification. The new feature method is able to describe tissue architecture based on glandular structure. It is based on statistically representing the relative distribution of tissue components around lumen regions, while preserving spatial and quantitative information, as a basis for diagnosing and analyzing different areas within an image. We demonstrate the efficacy of the method in extracting discriminative features for obtaining high classification rates for tubular formation in both healthy and cancerous tissue, which is an important component in Gleason and tubule-based Elston grading. The proposed method may be used for glandular classification, also in other tissue types, in addition to general applicability as a region-based feature descriptor in image analysis where the image represents a bag with a certain label (or grade) and the region-based feature vectors represent instances.
Year
DOI
Venue
2015
10.1155/2015/943104
Periodicals
Field
DocType
Volume
Computer vision,Feature descriptor,Feature vector,Generalization,Computer science,Sampling (statistics),Biological tissue,Artificial intelligence,Contextual image classification,Discriminative model,Tubular formation
Journal
2015
Issue
ISSN
Citations 
1
1687-4188
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Jimmy C. Azar100.34
Martin Simonsson200.34
ewert bengtsson313525.36
Anders Hast432.12