Effective brain segmentation method based on MR physics image processing
- Authors
- Jung, E.-Y.; Eun, S.-J.; Park, D.K.
- Issue Date
- 2013
- Keywords
- Active contour model; Brain segmentation; Curve fitting; MR theory; Susceptibility weighted imaging (SWI)
- Citation
- International Journal of Bio-Science and Bio-Technology, v.5, no.4, pp.201 - 213
- Journal Title
- International Journal of Bio-Science and Bio-Technology
- Volume
- 5
- Number
- 4
- Start Page
- 201
- End Page
- 213
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/14941
- ISSN
- 2233-7849
- Abstract
- Object recognition is usually processed based on region segmentation algorithm. Region segmentation in the IT field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective region segmentation method based on Susceptibility Weighted Imaging (SWI) within the magnetic resonance (MR) theory. When we do pre-processing, proposed method was composed of SWI process. And then we do the Gray-white matter segmentation by Active Contour Model(ACM) in post-processing. In this study, the experiment had been conducted using images including the brain region and by getting up contrast enhancement image of SWI for segmentation to extract region (white matter) segmentation even when the border line was not clear. As a result, an average area difference of 8.2%, which was higher than the accuracy of conventional region segmentation algorithm, was obtained.
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