Classification Of Human Knee Data From Magnetic Resonance Images
Reyes-Aldasoro, C.C. and Bhalerao, A.H. (2002) Classification Of Human Knee Data From Magnetic Resonance Images. Technical Report. University of Warwick, Coventry, UK.
- Published Version
This report considers the general problem of segmentation of Magnetic Resonance Images. The final objective is to correctly assign a unique label or class which represents an anatomical structure to every pixel or voxel in a data set. The images analysed describe a human knee scanned by Magnetic Resonance (MR). A brief description of the anatomy of the knee and physics of MR imaging is given. A review of image segmentation approaches, focusing on multiresolution and texture segmentation, follows. The first segmentation technique implemented here is grey level thresholding, which is later improved by adding two other descriptors of the images: standard deviation and a moment from the co-occurrence matrix. Frequency analysis through sub-band filtering is proposed as a way to improve the description of the textural regions and boundaries between anatomical regions. Comparative results of the different techniques are presented and finally conclusions and future work is proposed.
|Item Type:||Monograph (Technical Report)|
|Uncontrolled Keywords:||pcav hpsg image signal processing mri magnetic resonance imaging|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
|Divisions:||Faculty of Science > Computer Science|
|Depositing User:||Simon Hammond|
|Date Deposited:||19 Jun 2011 20:47|
|Last Modified:||23 Feb 2012 09:09|
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