Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/807
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dc.contributor.authorDaba, Jihad S.en_US
dc.date.accessioned2020-12-23T08:37:27Z-
dc.date.available2020-12-23T08:37:27Z-
dc.date.issued2002-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/807-
dc.description.abstractIn this work, we devise a segmentation scheme aimed at extracting edge information from speckled images using a maximum likelihood edge detector. The scheme was based on finding a threshold for the probability density function of the summing average field over a neighborhood set. A rigorous stochastic analysis was used to derive an exact expression for the cumulative density function of the pdf of the averaging sum image. Based on this, an accurate prob. of error was derived and the performance of the scheme was analysed. The segmentation performed reasonably well for both simulated and clinical images. The importance of this work is the development of a stochastic-based segmentation, allowing an accurate quantification of the prob. of false detection. Non visual quantification and misclassification in medical images is relatively new and is of interest to the clinician.en_US
dc.format.extent3 p.en_US
dc.language.isoengen_US
dc.titleSegmentation of speckled ultrasound images based on a statistical modelen_US
dc.typeConference Paperen_US
dc.relation.conferenceEURASIP International Conference (Biosignal2002) (16th : June 2002 : Brno, Czech Republic)en_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.date.catalogued2018-02-08-
dc.description.statusPublisheden_US
dc.identifier.OlibID177553-
dc.provenance.recordsourceOliben_US
Appears in Collections:Department of Electrical Engineering
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