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Advances in Artificial Intelligence
Volume 2010 (2010), Article ID 520427, 15 pages
http://dx.doi.org/10.1155/2010/520427
Research Article

3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches

1Department of Electronic & Computer Engineering, School of Engineering and Design, Brunel University, West London, Uxbridge UB8 3PH, UK
2Nanotechnology and Integrated BioEngineering Centre (NIBEC), Faculty of Computing and Engineering, University of Ulster, Shore Road Newtownabbey Co. Antrim BT37 0QB, Northern Ireland

Received 8 February 2010; Revised 24 May 2010; Accepted 24 June 2010

Academic Editor: Chu-Song Chen

Copyright © 2010 Shadi AlZu'bi and Abbes Amira. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations.