A Parallel Implementation of 4-Dimensional Haralick Texture Analysis for Disk-Resident Image Datasets

Texture analysis is one possible method to detect features in biomedical images. During texture analysis, texture related information is found by examining local variations in image brightness. 4-dimensional (4D) Haralick texture analysis is a method that extracts local variations along space and time dimensions and represents them as a collection of fourteen statistical parameters. However, the application of the 4D Haralick method on large time-dependent 2D and 3D image datasets is hindered by computation and memory requirements. This paper presents a parallel implementation of 4D Haralick texture analysis on PC clusters. We present a performance evaluation of our implementation on a cluster of PCs. Our results show that good performance can be achieved for this application via combined use of task- and data-parallelism.