Hardware implementation and power analysis of HWT for medical imaging

Processing large images requires a large amount of memory, power hungry and computationally intensive which causes a high demand for an efficient algorithm implementation to overcome the problems. This paper discusses an efficient hardware implementation of the Haar wavelet transform (HWT) core on reconfigurable platforms. The proposed HWT implementation can be explored for automatic detection and segmentation of tumour in medical images. The HWT algorithm has been demonstrated for the segmentation of phantom data and its hardware implementation has been carried out using Handel C on different field programmable gate arrays (FPGAs) devices. Results obtained for the implementation performance in terms of area and power have been evaluated and compared with other existing systems for HWT computation.

[1]  Jason Helge Anderson,et al.  Active leakage power optimization for FPGAs , 2006, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[2]  Michel Barlaud,et al.  Image coding using wavelet transform , 1992, IEEE Trans. Image Process..

[3]  Richard L. Van Metter,et al.  Handbook of Medical Imaging , 2009 .

[4]  David N. Kennedy,et al.  Neuroanatomical Segmentation in MRI: Technological Objectives , 1997, Int. J. Pattern Recognit. Artif. Intell..

[5]  Kashif Rajpoot,et al.  Hyperspectral colon tissue cell classification , 2004 .

[6]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Harold L. Kundel,et al.  Handbook of Medical Imaging, Volume 1. Physics and Psychophysics , 2000 .

[8]  Yaguang Chen,et al.  Multiresolution Medical Image Segmentation Based on Wavelet Transform , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[9]  J. Todd.  Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

[10]  Andrzej Sluzek,et al.  Power Optimization Techniques in FPGA Devices: A Combination of System- and Low-Levels , 2007 .

[11]  D. Dearnaley,et al.  Magnetic resonance imaging (MRI): considerations and applications in radiotherapy treatment planning. , 1997, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[12]  S. Lawrie,et al.  Brain abnormality in schizophrenia , 1998, British Journal of Psychiatry.

[13]  Abbes Amira,et al.  Power Modeling and Efficient FPGA Implementation of FHT for Signal Processing , 2007, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.