Ensemble based system for whole-slide prostate cancer probability mapping using color texture features.

Hdl Handle:
http://hdl.handle.net/10147/229196
Title:
Ensemble based system for whole-slide prostate cancer probability mapping using color texture features.
Authors:
DiFranco, Matthew D; O'Hurley, Gillian; Kay, Elaine W; Watson, R William G; Cunningham, Padraig
Affiliation:
School of Computer Science and Informatics, University College Dublin, Ireland.
Citation:
Ensemble based system for whole-slide prostate cancer probability mapping using color texture features., 35 (7-8):629-45 Comput Med Imaging Graph
Journal:
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Issue Date:
2011
URI:
http://hdl.handle.net/10147/229196
DOI:
10.1016/j.compmedimag.2010.12.005
PubMed ID:
21269807
Abstract:
We present a tile-based approach for producing clinically relevant probability maps of prostatic carcinoma in histological sections from radical prostatectomy. Our methodology incorporates ensemble learning for feature selection and classification on expert-annotated images. Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results. Ensembles of classifiers are built using expert-annotated tiles from training images, and scores for the probability of cancer presence are calculated from the responses of each classifier in the ensemble. Spatial filtering of tile-based texture features prior to classification results in increased heat-map coherence as well as AUC values of 95% using ensembles of either random forests or support vector machines. Our approach is designed for adaptation to different imaging modalities, image features, and histological decision domains.
Item Type:
Article
Language:
en
MeSH:
Algorithms; Color; Histological Techniques; Humans; Image Interpretation, Computer-Assisted; Male; Pattern Recognition, Automated; Prostatic Neoplasms
ISSN:
1879-0771

Full metadata record

DC FieldValue Language
dc.contributor.authorDiFranco, Matthew Den_GB
dc.contributor.authorO'Hurley, Gillianen_GB
dc.contributor.authorKay, Elaine Wen_GB
dc.contributor.authorWatson, R William Gen_GB
dc.contributor.authorCunningham, Padraigen_GB
dc.date.accessioned2012-06-15T15:34:04Z-
dc.date.available2012-06-15T15:34:04Z-
dc.date.issued2011-
dc.identifier.citationEnsemble based system for whole-slide prostate cancer probability mapping using color texture features., 35 (7-8):629-45 Comput Med Imaging Graphen_GB
dc.identifier.issn1879-0771-
dc.identifier.pmid21269807-
dc.identifier.doi10.1016/j.compmedimag.2010.12.005-
dc.identifier.urihttp://hdl.handle.net/10147/229196-
dc.description.abstractWe present a tile-based approach for producing clinically relevant probability maps of prostatic carcinoma in histological sections from radical prostatectomy. Our methodology incorporates ensemble learning for feature selection and classification on expert-annotated images. Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results. Ensembles of classifiers are built using expert-annotated tiles from training images, and scores for the probability of cancer presence are calculated from the responses of each classifier in the ensemble. Spatial filtering of tile-based texture features prior to classification results in increased heat-map coherence as well as AUC values of 95% using ensembles of either random forests or support vector machines. Our approach is designed for adaptation to different imaging modalities, image features, and histological decision domains.en_GB
dc.language.isoenen
dc.rightsArchived with thanks to Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Societyen_GB
dc.subject.meshAlgorithms-
dc.subject.meshColor-
dc.subject.meshHistological Techniques-
dc.subject.meshHumans-
dc.subject.meshImage Interpretation, Computer-Assisted-
dc.subject.meshMale-
dc.subject.meshPattern Recognition, Automated-
dc.subject.meshProstatic Neoplasms-
dc.titleEnsemble based system for whole-slide prostate cancer probability mapping using color texture features.en_GB
dc.typeArticleen
dc.contributor.departmentSchool of Computer Science and Informatics, University College Dublin, Ireland.en_GB
dc.identifier.journalComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Societyen_GB
dc.description.provinceLeinsteren

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