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An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing

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Abstract

In the existing segmentation algorithms, most of them take single pixel as processing unit and segment an image mainly based on the gray value information of the image pixels. However, the spatially structural information between pixels of an image provides even more important information of the image. In order to effectively exploit both the gray value and the spatial information of image pixels, this paper proposes a fusion method for image segmentation by jointly utilizing vector quantization and edge detection methods. In the method, the image to be segmented is divided into small sub-blocks with each sub-block constituting a vector and the vectors are classified into two patterns, called the edge pattern and non-edge pattern, by using an edge detection algorithm. The image is then processed further, in which a Boundary Detection (BD) algorithm is developed for extracting the refined boundary curves in the edge pattern vectors and a Vector Quantization (VQ) approach is presented for segmenting the non-edge pattern vectors. In addition, an SOM neural network is proposed for realizing the VQ algorithm adaptively. Finally, a fusion scheme is designed to synthesize the results of VQ and BD to accomplish the segmentation. Simulation experiments and comparison studies have been conducted with applications to medical image processing in the paper, and the results validate the effectiveness of the proposed method.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116023, China

    Ailing De & Chengan Guo

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  1. Ailing De
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  2. Chengan Guo
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Correspondence to Ailing De.

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De, A., Guo, C. An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing. Int. J. Mach. Learn. & Cyber. 5, 543–551 (2014). https://doi.org/10.1007/s13042-013-0205-1

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  • DOI: https://doi.org/10.1007/s13042-013-0205-1

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