Structure and Context in Prostatic Gland Segmentation and Classification View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Kien Nguyen , Anindya Sarkar , Anil K. Jain

ABSTRACT

A novel gland segmentation and classification scheme applied to an H&E histology image of the prostate tissue is proposed. For gland segmentation, we associate appropriate nuclei objects with each lumen object to create a gland segment. We further extract 22 features to describe the structural information and contextual information for each segment. These features are used to classify a gland segment into one of the three classes: artifact, normal gland and cancer gland. On a dataset of 48 images at 5× magnification (which includes 525 artifacts, 931 normal glands and 1,375 cancer glands), we achieved the following classification accuracies: 93% for artifacts v. true glands; 79% for normal v. cancer glands, and 77% for discriminating all three classes. The proposed method outperforms state of the art methods in terms of segmentation and classification accuracies and computational efficiency. More... »

PAGES

115-123

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-33415-3_15

DOI

http://dx.doi.org/10.1007/978-3-642-33415-3_15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036127728

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23285542


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