Please use this identifier to cite or link to this item: http://ir.unikl.edu.my/jspui/handle/123456789/14392
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dc.contributor.authorGan, Hong-Seng-
dc.contributor.authorAhmad Helmy Abdul Karim-
dc.contributor.authorKhairil Amir Sayuti-
dc.contributor.authorTan, Tian-Swee-
dc.contributor.authorMohammed Rafiq Abdul Kadir-
dc.contributor.authorUniKL BMI-
dc.date.accessioned2016-09-23T02:07:38Z-
dc.date.available2016-09-23T02:07:38Z-
dc.date.issued2016-09-23-
dc.identifier.urihttp://ir.unikl.edu.my/jspui/handle/123456789/14392-
dc.descriptionUniKL BMIen_US
dc.description.abstractUnlike automated segmentation, the accuracy of semi-automated segmentation is affected by pertinent parameters such as observer, type of methods and type of cartilage. In this paper, we investigated the effect of these parameters on segmentation results. based on Dice similarity index obtained from fifteen normal and ten diseased magnetic resonance images, a parameter estimation model was constructed to study the impact of each parameter.en_US
dc.language.isoenen_US
dc.titleAnalysis of Parameters' Effects in Semi-Automated Knee Cartilage Segmentation Modelen_US
dc.typeWorking Paperen_US
dc.conference.nameInternational Conference on Mathematics Engineering & Industrial Applicationsen_US
dc.conference.year2016en_US
Appears in Collections:Conference Papers



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