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The Study of Fuzzy Clustering Segmentation Methods of Brain MRI

Author: NieBin
Tutor: SunZhongLin；NieShengDong
School: Shandong University of Science and Technology
Course: Software Engineering
Keywords: HEAD MRI Segmentation Fuzzy Clustering K- means clustering Fast fuzzy clustering
CLC: TP391.41
Type: Master's thesis
Year: 2005
Downloads: 260
Quote: 1
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Abstract


Medical image segmentation is a classical problem in the field of medical image processing, and also the impact of medical images are widely used in clinical bottlenecks, such as three-dimensional reconstruction, quantitative analysis and visualization. Complex medical images such as magnetic resonance images of the brain, because the brain tissue between aliasing together there is no clear boundary, the differences between the different individuals, coupled with the inhomogeneity of the magnetic field in the imaging process. the image inherent uncertainty caused by the partial volume effect and the effect of noise, so split the problem is much more complex and difficult. Detailed overview and discussion of the currently used method of MR image segmentation based on the characteristics of the MR images based on fuzzy clustering technique, MR brain image segmentation method. Fuzzy clustering technology is well suited to deal with the inherent uncertainty of things, and is less sensitive to noise; using less accurate way to describe the complex system, clear images of the boundary segmentation; It mathematics valued logic convert continuous-valued logic, so that it is closer to the human way of thinking. Therefore, magnetic resonance images of the brain is so fuzzy boundaries of image segmentation technique based on fuzzy clustering is highly targeted. Visible Human data set of cross-sectional magnetic resonance brain images as the object of study. Brain tissue extracted white matter, gray matter and cerebrospinal fluid from the brain image, the implementation of segmentation of MR brain images using image segmentation method based on fuzzy clustering technique. According to the characteristics of ordinary fuzzy clustering algorithm run slow, fuzzy clustering algorithm. Under the premise of maintaining the the ordinary fuzzy clustering accuracy, the algorithm can effectively improve the speed of fuzzy clustering, which single-spectral and multi-spectral image segmentation speed is increased by more than 6 times and 3 times, respectively. Selection and initialization of the parameters of fuzzy clustering to MR image segmentation using fuzzy clustering technique is discussed in detail, and initially given parameter select and initialization programs. In order to get the split brain tissue is closer to the actual anatomical structure, the the single spectral MR images organized into multi-spectral MR images, and multi-spectral MR image segmentation. MR image is the only medical images with multi-spectral image features. Multispectral images can provide more than a single spectral image tissue anatomy information. A large number of experiments show that in the image segmentation, multi-spectral characteristics of the MR image segmentation results than single spectral image is closer to the actual tissue anatomy.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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