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Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

2012-07Preprint Open access
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dc.contributor.authorAcharya, U. R.
dc.contributor.authorSree, S. V.
dc.contributor.authorRibeiro, Ricardo
dc.contributor.authorKrishnamurthi, G.
dc.contributor.authorMarinho, Rui
dc.contributor.authorSanches, João
dc.contributor.authorSuri, J. S.
dc.date.accessioned2013-12-16T18:06:20Z
dc.date.available2013-12-16T18:06:20Z
dc.date.issued2012-07
dc.description.abstractPURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.por
dc.identifier.citationAcharya UR, Sree SV, Ribeiro R, Krishnamurthi G, Marinho R, Sanches J, et al. Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm. Med Phys. 2012 Jul;39(7):4255-64.por
dc.identifier.issn0094-2405
dc.identifier.urihttp://hdl.handle.net/10400.21/3012
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherAIPpor
dc.relation.publisherversionhttp://scitation.aip.org/content/aapm/journal/medphys/39/7/10.1118/1.4725759por
dc.subjectMedical imagingpor
dc.subjectUltrasonographypor
dc.subjectLiverpor
dc.subjectEntropypor
dc.subjectWaveletspor
dc.subjectWavelet transformpor
dc.subjectComputed tomographypor
dc.subjectComputer aided diagnosispor
dc.subjectComputer softwarepor
dc.subjectImage analysispor
dc.titleData mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigmpor
dc.typepreprint
dspace.entity.typePublication
oaire.citation.endPage4264por
oaire.citation.startPage4255por
oaire.citation.titleMedical Physicspor
oaire.citation.volume39por
rcaap.rightsrestrictedAccesspor
rcaap.typepreprintpor

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