Parallel four-dimensional Haralick texture analysis for disk-resident image datasets

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.