Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform.

Abstract:

:This paper describes a new approach for texture characterization, based on nonseparable wavelet decomposition, and its application for the discrimination of visually similar diffuse diseases of liver. The proposed feature-extraction algorithm applies nonseparable quincunx wavelet transform and uses energies of the transformed regions to characterize textures. Classification experiments on a set of three different tissue types show that the scale/frequency approach, particularly one based on the nonseparable wavelet transform, could be a reliable method for a texture characterization and analysis of B-scan liver images. Comparison between the quincunx and the traditional wavelet decomposition suggests that the quincunx transform is more appropriate for characterization of noisy data, and practical applications, requiring description with lower rotational sensitivity.

journal_name

IEEE Trans Med Imaging

authors

Mojsilović A,Popović M,Marković S,Krstić M

doi

10.1109/42.730399

subject

Has Abstract

pub_date

1998-08-01 00:00:00

pages

541-9

issue

4

eissn

0278-0062

issn

1558-254X

journal_volume

17

pub_type

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