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Conference Paper: Automatic detection of malignant prostatic gland units in cross-sectional microscopic images

TitleAutomatic detection of malignant prostatic gland units in cross-sectional microscopic images
Authors
KeywordsClassification
Histological Images
Prostate glands
Segmentation
Issue Date2010
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349
Citation
The 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 1057-1060 How to Cite?
AbstractProstate cancer is the second most frequent cause of cancer deaths among men in the US. In the most reliable screening method, histological images from a biopsy are examined under a microscope by pathologists. In an early stage of prostate cancer, only relatively few gland units in a large region become malignant. Discovering such sparse malignant gland units using a microscope is a labor-intensive and error-prone task for pathologists. In this paper, we develop effective image segmentation and classification methods for automatic detection of malignant gland units in microscopic images. Both segmentation and classification methods are based on carefully designed feature descriptors, including color histograms and texton co-occurrence tables. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/139999
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorXia, Ten_HK
dc.contributor.authorYu, Yen_HK
dc.contributor.authorHua, Jen_HK
dc.date.accessioned2011-09-23T06:04:31Z-
dc.date.available2011-09-23T06:04:31Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 1057-1060en_HK
dc.identifier.issn1522-4880en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139999-
dc.description.abstractProstate cancer is the second most frequent cause of cancer deaths among men in the US. In the most reliable screening method, histological images from a biopsy are examined under a microscope by pathologists. In an early stage of prostate cancer, only relatively few gland units in a large region become malignant. Discovering such sparse malignant gland units using a microscope is a labor-intensive and error-prone task for pathologists. In this paper, we develop effective image segmentation and classification methods for automatic detection of malignant gland units in microscopic images. Both segmentation and classification methods are based on carefully designed feature descriptors, including color histograms and texton co-occurrence tables. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349en_HK
dc.relation.ispartofProceedings of the International Conference on Image Processing, ICIP 2010en_HK
dc.rightsInternational Conference on Image Processing Proceedings. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectClassificationen_HK
dc.subjectHistological Imagesen_HK
dc.subjectProstate glandsen_HK
dc.subjectSegmentationen_HK
dc.titleAutomatic detection of malignant prostatic gland units in cross-sectional microscopic imagesen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYu, Y:yzyu@cs.hku.hken_HK
dc.identifier.authorityYu, Y=rp01415en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICIP.2010.5650763en_HK
dc.identifier.scopuseid_2-s2.0-78651095236en_HK
dc.identifier.hkuros194319en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78651095236&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage1057en_HK
dc.identifier.epage1060en_HK
dc.identifier.isiWOS:000287728001040-
dc.publisher.placeUnited Statesen_HK
dc.description.otherThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 1057-1060-
dc.identifier.scopusauthoridXia, T=35876042700en_HK
dc.identifier.scopusauthoridYu, Y=8554163500en_HK
dc.identifier.scopusauthoridHua, J=7102121257en_HK

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