3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches
Joint Authors
Source
Advances in Artificial Intelligence
Issue
Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2010-08-02
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
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.
American Psychological Association (APA)
AlZu'bi, Shadi& Amira, Abbes. 2010. 3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches. Advances in Artificial Intelligence،Vol. 2010, no. 2010, pp.1-15.
https://search.emarefa.net/detail/BIM-478257
Modern Language Association (MLA)
AlZu'bi, Shadi& Amira, Abbes. 3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches. Advances in Artificial Intelligence No. 2010 (2010), pp.1-15.
https://search.emarefa.net/detail/BIM-478257
American Medical Association (AMA)
AlZu'bi, Shadi& Amira, Abbes. 3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches. Advances in Artificial Intelligence. 2010. Vol. 2010, no. 2010, pp.1-15.
https://search.emarefa.net/detail/BIM-478257
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-478257