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Content Provider | IEEE Xplore Digital Library |
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Author | Kim Wang Jianhua Wu Zhaoyu Pian Li Guo Liqun Gao |
Copyright Year | 2007 |
Description | Author affiliation: Northeastern Univ., Shenyang (Kim Wang; Jianhua Wu; Zhaoyu Pian; Li Guo; Liqun Gao) |
Abstract | Medical image edge detection is an important work for object recognition of human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, edge is detected according to some early brought forward algorithms such as gradient-based algorithm, but they are not so good for noise medical image edge detection. In this paper, based on multi-scale morphology algorithm is proposed to detect the edge of brain MRI with Gaussian and salt & pepper noise. Threshold is to classify the MRI into two opposite classes: object and background. The part which is larger than the threshold uses large-scale structure element to detect the edge, and uses small-scale structure element for the part less than the threshold. Large-scale structure element gets through small-scale structure element's dilation. The experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used gradient-based edge detecting algorithms. |
Starting Page | 2437 |
Ending Page | 2440 |
File Size | 953134 |
Page Count | 4 |
File Format | |
ISBN | 9781424408177 |
DOI | 10.1109/ICCA.2007.4376800 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-05-30 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Image edge detection Magnetic resonance Morphology Biomedical imaging Magnetic resonance imaging Large-scale systems Object recognition Humans Image segmentation Image reconstruction Mathematical Morphology edgedetection multi-scalestructure element Magnetic Resonance Images |
Content Type | Text |
Resource Type | Article |
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