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


Autoria(s): Acharya, U. R.; Sree, S. V.; Ribeiro, Ricardo; Krishnamurthi, G.; Marinho, Rui; Sanches, João; Suri, J. S.
Data(s)

16/12/2013

16/12/2013

01/07/2012

Resumo

PURPOSE: 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.

Identificador

Acharya 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.

0094-2405

http://hdl.handle.net/10400.21/3012

Idioma(s)

eng

Publicador

AIP

Relação

http://scitation.aip.org/content/aapm/journal/medphys/39/7/10.1118/1.4725759

Direitos

restrictedAccess

Palavras-Chave #Medical imaging #Ultrasonography #Liver #Entropy #Wavelets #Wavelet transform #Computed tomography #Computer aided diagnosis #Computer software #Image analysis
Tipo

preprint