3D Multiresolution Analysis for reduced features segmentation of medical volumes using PCA

3D volume segmentation aims at partitioning the voxels into 3D objects (sub-volumes) which represent meaningful physical entities. Multi-resolution analysis (MRA) allows for the preservation of an image according to certain levels of resolution or blurring. The quality of this approach makes it usef...

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Bibliographic Details
Published inAPCCAS 2010-2010 IEEE Asia Pacific Conference on Circuits and Systems pp. 604 - 607
Main Authors Zu'bi, S A, Islam, N, Abbod, M
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Biomedical imaging
Feature Reduction
Image segmentation
Medical images
Multiresolution analysis
PCA
Principal component analysis
Three dimensional displays
Wavelet
Wavelet transforms
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Summary:3D volume segmentation aims at partitioning the voxels into 3D objects (sub-volumes) which represent meaningful physical entities. Multi-resolution analysis (MRA) allows for the preservation of an image according to certain levels of resolution or blurring. The quality of this approach makes it useful in image compression, de-noising, and classification or segmentation. This paper focuses on the implementation of a medical volume segmentation technique using 3D Discrete Wavelet Transform (3D-DWT). Principle Component Analysis (PCA) has been presented to reduce the dimensionality of the 3D volume as a pre-processing step of 3D-DWT to accelerate the segmentation process.
ISBN:142447454X
9781424474547
DOI:10.1109/APCCAS.2010.5774847