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Parallel four‐dimensional Haralick texture analysis for disk‐resident image datasets
Author(s) -
Woods Brent,
Clymer Bradley,
Heverhagen Johannes,
Knopp Michael,
Saltz Joel,
Kurc Tahsin
Publication year - 2007
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.1073
Subject(s) - computer science , texture (cosmology) , texture filtering , image texture , texture compression , artificial intelligence , image retrieval , brightness , image (mathematics) , pattern recognition (psychology) , computation , computer vision , texture memory , image processing , algorithm , software rendering , 3d computer graphics , physics , computer graphics , optics
Texture analysis is one possible method of detecting features in biomedical images. During texture analysis, texture‐related information is found by examining local variations in image brightness. Four‐dimensional (4D) Haralick texture analysis is a method that extracts local variations along space and time dimensions and represents them as a collection of 14 statistical parameters. However, application of the 4D Haralick method on large time‐dependent image datasets is hindered by data retrieval, computation, and memory requirements. This paper describes a parallel implementation using a distributed component‐based framework of 4D Haralick texture analysis on PC clusters. The experimental performance results show that good performance can be achieved for this application via combined use of task‐ and data‐parallelism. In addition, we show that our 4D texture analysis implementation can be used to classify imaged tissues. Copyright © 2006 John Wiley & Sons, Ltd.