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An adaptive spatial clustering method for automatic brain MR image segmentation

Progress in Natural Science:Materials International 2009年第10期19卷 1373-1382页

作者： jingdan zhang,daoqing dai center for computer vision and department of mathematics,sun yat-sen(Zhongshan) university,guangzhou 510275,china [a]center for computer vision and department of mathematics Sun Yat-Sen (Zhongshan) University Guangzhou 510275 China

In this paper,an adaptive spatial clustering method is presented for automatic brain MR image segmentation,which is based on a competitive learning algorithm-self-organizing map(SOM).We use a pattern recognition approach in terms of feature generation and classifier ***,a multi-dimensional feature vector is constructed using local spatial ***,an adaptive spatial growing hierarchical SOM(ASGHSOM) is proposed as the classifier,which is an extension of SOM,fusing multi-scale segmentation with the competitive learning clustering algorithm to overcome the problem of overlapping grey-scale intensities on boundary ***,an adaptive spatial distance is integrated with ASGHSOM,in which local spatial information is considered in the clustering process to reduce the noise effiect and the classification *** proposed method is validated by extensive experiments using both simulated and real MR data with varying noise level,and is compared with the state-of-the-art algorithms.

关键词： Self-organizing map Growing hierarchical SOM Adaptive spatial distance Image segmentation MR images

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Sparse discriminant analysis for breast cancer biomarker identification and classification

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Progress in Natural Science:Materials International 2009年第11期19卷 1635-1641页

作者： Yu Shi~(a,b),daoqing dai~(a,*),Chaochun Liu~(a,b),Hong Yan~(b,c) a center for computer vision and department of mathematics,sun yat-sen(Zhongshan) university,guangzhou 510275,china b department of Electric Engineering,City university of Hong Kong,83 Tat Chee Avenue,Kowloon,Hong Kong,china c School of Electrical and Information Engineering,university of Sydney,NSW 2006,Australia [a]center for computer vision and department of mathematics Sun Yat-Sen (Zhongshan) University Guangzhou 510275 China [b]department of Electric Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong China [c]School of Electrical and Information Engineering University of Sydney NSW 2006 Australia

Biomarker identification and cancer classification are two important procedures in microarray data *** propose a novel unified method to carry out both *** first preselect biomarker candidates by eliminating unrelated genes through the BSS/WSS ratio filter to reduce computational cost,and then use a sparse discriminant analysis method for simultaneous biomarker identification and cancer ***,we give a mathematical justification about automatic biomarker *** results show that the proposed method can identify key genes that have been verified in biochemical or biomedical research and classify the breast cancer type correctly.

关键词： Biomarker identification Cancer classification Discriminant analysis Maximum penalized likelihood Microarray data analysis

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