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dc.contributor.authorK. Mala
dc.contributor.authorV. Sadasivam
dc.contributor.authorS. Alagappan
dc.date.accessioned2017-05-09T00:25:18Z
dc.date.available2017-05-09T00:25:18Z
dc.date.copyrightJune, 2007
dc.date.issued2007
dc.identifier.issn1932-6181
dc.identifier.otherJMDOA4-27984#180_1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl/handle/yetl/136590
dc.description.abstractThe use of ultrasonography as an imaging modality has become widespread because of its ability to visualize the organs with no deleterious effects and low cost. Using ultrasonic liver images focal diseases can be identified by differences in echogenicity between normal and areas affected by diseases. In the presence of diffused disease, however, the entire organ may be affected. In that situation, there is no contrast in echo intensity on which to base a diagnosis. Hence it is difficult for an experienced clinician to diagnose the diffused liver diseases by simple visual interpretations. This can be improved by providing useful information, obtained by computer aided tissue characterization, that cannot be obtained by simple visual interpretation.
dc.publisherThe American Society of Mechanical Engineers (ASME)
dc.titleCombined Statistical and Multiscale View on Ultrasonic Liver Images for Characterization
dc.typeJournal Paper
dc.journal.volume1
dc.journal.issue2
dc.journal.titleJournal of Medical Devices
dc.identifier.doi10.1115/1.2735975
dc.journal.fristpage180
dc.journal.lastpage184
dc.identifier.eissn1932-619X
dc.keywordsLiver
dc.keywordsWavelets
dc.keywordsArtificial neural networks AND Design
dc.treeJournal of Medical Devices:;2007:;volume( 001 ):;issue: 002
dc.contenttypeFulltext


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