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. Copyright © 2006 John Wiley & Sons, Ltd.

[1]  G Brix,et al.  Pathophysiologic basis of contrast enhancement in breast tumors , 1999, Journal of magnetic resonance imaging : JMRI.

[2]  Joel H. Saltz,et al.  Distributed processing of very large datasets with DataCutter , 2001, Parallel Comput..

[3]  Michael Cox,et al.  Application-controlled demand paging for out-of-core visualization , 1997 .

[4]  V. Pascucci,et al.  Parallel accelerated isocontouring for out-of-core visualization , 1999, Proceedings 1999 IEEE Parallel Visualization and Graphics Symposium (Cat. No.99EX381).

[5]  M V Knopp,et al.  Dynamic contrast-enhanced magnetic resonance imaging in oncology. , 2001, Topics in magnetic resonance imaging : TMRI.

[6]  Joel H. Saltz,et al.  DataCutter: Middleware for Filtering Very Large Scientific Datasets on Archival Storage Systems , 2000, IEEE Symposium on Mass Storage Systems.

[7]  Cláudio T. Silva,et al.  External memory techniques for isosurface extraction in scientific visualization , 1998, External Memory Algorithms.

[8]  Henri Casanova,et al.  Netsolve: a Network-Enabled Server for Solving Computational Science Problems , 1997, Int. J. High Perform. Comput. Appl..

[9]  A. Padhani.  Dynamic contrast‐enhanced MRI in clinical oncology: Current status and future directions , 2002, Journal of magnetic resonance imaging : JMRI.

[10]  Joel H. Saltz,et al.  Design of a framework for data-intensive wide-area applications , 2000, Proceedings 9th Heterogeneous Computing Workshop (HCW 2000) (Cat. No.PR00556).

[11]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[13]  L. Schad,et al.  MR image texture analysis--an approach to tissue characterization. , 1993, Magnetic resonance imaging.

[14]  Kwan-Liu Ma,et al.  Out-of-Core Streamline Visualization on Large Unstructured Meshes , 1997, IEEE Trans. Vis. Comput. Graph..

[15]  Joel H. Saltz,et al.  A Parallel Implementation of 4-Dimensional Haralick Texture Analysis for Disk-Resident Image Datasets , 2004, Proceedings of the ACM/IEEE SC2004 Conference.

[16]  Peter A. Dinda,et al.  Preliminary Report on the Design of a Framework for Distributed Visualization , 1999, PDPTA.

[17]  G. Tourassi.  Journey toward computer-aided diagnosis: role of image texture analysis. , 1999, Radiology.

[18]  Tahsin M. Kurç,et al.  Image-Space Decomposition Algorithms for Sort-First Parallel Volume Rendering of Unstructured Grids , 2004, The Journal of Supercomputing.