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 provides even more important information of the image. In order to effectively exploit both the gray value and the spatial information of pixels, this paper proposes an image segmentation method based on Vector Quantization (VQ) technique. In the method, the image to be segmented is divided into small sub-blocks with each sub-block constituting a feature vector. Further, the vectors are classified through vector quantization. In addition, the self-organizing map (SOM) neural network is proposed for realizing the VQ algorithm adaptively. 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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De, A., Guo, C. (2013). A Vector Quantization Approach for Image Segmentation Based on SOM Neural Network. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_73
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DOI: https://doi.org/10.1007/978-3-642-39065-4_73
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