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Journal of Medical Imaging

Synthesis of intensity gradient and texture information for efficient three-dimensional segmentation of medical volumes
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Paper Abstract

We propose a framework that efficiently employs intensity, gradient, and textural features for three-dimensional (3-D) segmentation of medical (MRI/CT) volumes. Our methodology commences by determining the magnitude of intensity variations across the input volume using a 3-D gradient detection scheme. The resultant gradient volume is utilized in a dynamic volume growing/formation process that is initiated in voxel locations with small gradient magnitudes and is concluded at sites with large gradient magnitudes, yielding a map comprising an initial set of partitions (or subvolumes). This partition map is combined with an entropy-based texture descriptor along with intensity and gradient attributes in a multivariate analysis-based volume merging procedure that fuses subvolumes with similar characteristics to yield a final/refined segmentation output. Additionally, a semiautomated version of the aforestated algorithm that allows a user to interactively segment a desired subvolume of interest as opposed to the entire volume is also discussed. Our approach was tested on several MRI and CT datasets and the results show favorable performance in comparison to the state-of-the-art ITK-SNAP technique.

Paper Details

Date Published: 8 May 2015
PDF: 17 pages
J. Med. Img. 2(2) 024003 doi: 10.1117/1.JMI.2.2.024003
Published in: Journal of Medical Imaging Volume 2, Issue 2
Show Author Affiliations
Sreenath Rao Vantaram, Rochester Institute of Technology (United States)
Eli Saber, Rochester Institute of Technology (United States)
Sohail A. Dianat, Rochester Institute of Technology (United States)
Yang Hu, Rochester Institute of Technology (United States)


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