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dc.contributor.authorHuang, Hui-Lingen_US
dc.contributor.authorHsu, Ming-Hsinen_US
dc.contributor.authorLee, Hua-Chinen_US
dc.contributor.authorCharoenkwan, Phasiten_US
dc.contributor.authorHo, Shinn-Jangen_US
dc.contributor.authorHo, Shinn-Yingen_US
dc.date.accessioned2014-12-08T15:36:40Z-
dc.date.available2014-12-08T15:36:40Z-
dc.date.issued2013en_US
dc.identifier.isbn978-3-642-36543-0en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/11536/25015-
dc.description.abstractStudy of cellular senescence from images in molecular level plays an important role in understanding the molecular basis of ageing. It is desirable to know the morphological variation between young and senescent cells. This study proposes an ensemble support vector machine (SVM) based classifier with a novel set of image features to predict mouse senescence from HE-stain liver images categorized into four classes. For the across-subject prediction that all images of the same mouse are divided into training and test images, the test accuracy is as high as 97.01% by selecting an optimal set of informative image features using an intelligent genetic algorithm. For the leave-one-subject-out prediction that the test mouse is not involved in the training images of 20 mice, we identified eight informative feature sets and established eight SVM classifiers with a single feature set. The best accuracy of using an SVM classifier is 71.73% and the ensemble classifier consisting of these eight SVM classifiers can advance performance with accuracy of 80.95%. The best two feature sets are the gray level correlation matrix for describing texture and Haralick texture set, which are good morphological features in studying cellular senescence.en_US
dc.language.isoen_USen_US
dc.subjectAgingen_US
dc.subjectcellular senescenceen_US
dc.subjectfeature selectionen_US
dc.subjectgenetic algorithmen_US
dc.subjectHE-stainen_US
dc.subjectimage analysisen_US
dc.subjectpredictionen_US
dc.subjectSVMen_US
dc.titlePrediction of Mouse Senescence from HE-Stain Liver Images Using an Ensemble SVM Classifieren_US
dc.typeProceedings Paperen_US
dc.identifier.journalINTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2013), PT IIen_US
dc.citation.volume7803en_US
dc.citation.spage325en_US
dc.citation.epage334en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.identifier.wosnumberWOS:000340592600034-
Appears in Collections:Conferences Paper


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