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Clustering algorithms have been popularly applied in tissue segmentation in MRI. However, traditional clustering algorithms could not take advantage of some prior knowledge of data even when it does exist. In this paper, we propose a new approach to tissue segmentation of 3D brain MRI using semi-supervised spectral clustering. Spectral clustering algorithm is more powerful than traditional clustering...
Spectral clustering (SC), as an unsupervised learning algorithm, has been used successfully in the field of computer vision for data clustering. In some applications, however, background prior knowledge can be easily obtained, such as pairwise constraints. Therefore, semi-supervised learning is getting increasing attention in recent years. In this paper, a new algorithm named self-tuning semi-supervised...
Multiobjective evolutionary clustering approach has been successfully utilized in data clustering. In this paper, we propose a novel unsupervised machine learning algorithm namely multiobjective evolutionary clustering ensemble algorithm (MECEA) to perform the texture image segmentation. MECEA comprises two main phases. In the first phase, MECEA uses a multiobjective evolutionary clustering algorithm...
A new clustering approach namely immune spectral clustering algorithm (ISCA) is proposed in this paper. It combines spectral clustering with immune algorithm for data clustering. In this algorithm, making use of the dimension reduction ability of the spectral clustering algorithm, an immune clonal clustering algorithm is used to cluster the data points in the mapping space. Because we can get tight...
The watershed transform is a well-established tool for image segmentation. However, watershed segmentation is often not effective for textural images. In this paper, we describe an improved watershed segmentation algorithm combined with texture features. The aim of this study is to improve the generalization of watershed techniques and to construct a well segmentation of textural images. The method...
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