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论文类型：期刊论文

发表时间：2014-08-01

发表刊物：INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS

收录刊物：SCIE、EI、Scopus

卷号：5

期号：4

页面范围：543-551

ISSN号：1868-8071

关键字：Image segmentation; Spatial feature; Vector quantization; Edge detection; Information fusion

摘要：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.