Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2112
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dc.contributor.authorDaba, Jihad S.en_US
dc.date.accessioned2020-12-23T09:06:34Z-
dc.date.available2020-12-23T09:06:34Z-
dc.date.issued2007-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/2112-
dc.description.abstractIn this work, we improve a previously developed 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 a new kernel defined as the arithmetic mean-to-geometric mean ratio field over a circular neighborhood set and, in a general context, is founded on a likelihood random field model (LRFM). The segmentation algorithm was applied to discriminated speckle areas obtained using simple elliptic discriminant functions based on measures of the signal-to-noise ratio with fractional order moments. A rigorous stochastic analysis was used to derive an exact expression for the cumulative density function of the probability density function of the random field. Based on this, an accurate probability of error was derived and the performance of the scheme was analysed. The improved segmentation scheme performed well for both simulated and real images and showed superior results to those previously obtained using the original LRFM scheme and standard edge detection methods. In particular, the false alarm probability was markedly lower than that of the original LRFM method with oversegmentation artifacts virtually eliminated. The importance of this work lies in the development of a stochastic-based segmentation, allowing an accurate quantification of the probability of false detection. Non visual quantification and misclassification in medical ultrasound speckled images is relatively new and is of interest to clinicians.en_US
dc.format.extent3 p.en_US
dc.language.isoengen_US
dc.subjectDiscriminant functionen_US
dc.subjectFalse alarmen_US
dc.subjectSegmentationen_US
dc.subjectSignal-to-noise ratioen_US
dc.subjectSkewnessen_US
dc.subject.lcshSpeckleen_US
dc.titleImproved Segmentation of Speckled Images Using an Arithmetic-to-Geometric Mean Ratio Kernelen_US
dc.typeJournal Articleen_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.description.volume1en_US
dc.description.issue10en_US
dc.description.startpage1454en_US
dc.description.endpage1457en_US
dc.date.catalogued2017-11-13-
dc.description.statusPublisheden_US
dc.identifier.OlibID174922-
dc.identifier.openURLhttp://waset.org/publications/396/improved-segmentation-of-speckled-images-using-an-arithmetic-to-geometric-mean-ratio-kernelen_US
dc.relation.ispartoftextInternational journal of electrical robotics, electronics and communications engineeringen_US
dc.provenance.recordsourceOliben_US
Appears in Collections:Department of Electrical Engineering
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