Automated Classification of Glandular Tissue by Statistical Proximity Sampling
Joint Authors
Simonsson, Martin
Bengtsson, Ewert
Azar, Jimmy C.
Hast, Anders
Source
International Journal of Biomedical Imaging
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-01-18
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Due 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.
American Psychological Association (APA)
Azar, Jimmy C.& Simonsson, Martin& Bengtsson, Ewert& Hast, Anders. 2015. Automated Classification of Glandular Tissue by Statistical Proximity Sampling. International Journal of Biomedical Imaging،Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1065287
Modern Language Association (MLA)
Azar, Jimmy C.…[et al.]. Automated Classification of Glandular Tissue by Statistical Proximity Sampling. International Journal of Biomedical Imaging No. 2015 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1065287
American Medical Association (AMA)
Azar, Jimmy C.& Simonsson, Martin& Bengtsson, Ewert& Hast, Anders. Automated Classification of Glandular Tissue by Statistical Proximity Sampling. International Journal of Biomedical Imaging. 2015. Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1065287
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1065287