| dc.creator |
Alzubi, S |
|
| dc.creator |
Amira, A |
|
| dc.date |
2010-11-18T11:14:10Z |
|
| dc.date |
2010-11-18T11:14:10Z |
|
| dc.date |
2010 |
|
| dc.date.accessioned |
2022-05-25T13:40:52Z |
|
| dc.date.available |
2022-05-25T13:40:52Z |
|
| dc.identifier |
Advances in Artificial Intelligence, 2010: 520427, 2010 |
|
| dc.identifier |
1687-7470 |
|
| dc.identifier |
http://bura.brunel.ac.uk/handle/2438/4583 |
|
| dc.identifier |
http:dx.doi.org/10.1155/2010/520427 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/167538 |
|
| dc.description |
This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira. |
|
| dc.description |
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. |
|
| dc.language |
en |
|
| dc.publisher |
Hindawi Publishing Corporation |
|
| dc.title |
3D medical volume segmentation using hybrid multiresolution statistical approaches |
|
| dc.type |
Research Paper |
|