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Medical Image Edge Detection Based on EMD Method

Wuhan University Journal of Natural Sciences 2006年第5期11卷 1287-1291页

作者： peng shichun liu jian yan guoping Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan430074 Hubei China

As a new image analysis technique, Empirical Mode Decomposition （EMD） has been drawn more attention in recent years. In this paper, we proposed a fast EMD method for the edge detection of medical images. We implemented the method in the following steps： a） Decompose the original medical image with the image pyramid technique; b） Implement the EMD at the low resolution level image; c） Map the Intrinsic Mode Functions （IMFs） into the original image; d） Use the edge detector in a coarse IMF at the beginning of the procedure; e） Trace the detected result to the finest IMF to obtain the final image edge. Experimental results demonstrated the effectiveness of the proposed method.

关键词： edge detection empirical mode decomposition intrinsic mode function image pyramid multl-resolution map

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Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis

引用

Science China Chemistry 2008年第2期51卷 166-170页

作者： LIANG GuiZhao CHEN ZeCong yanG ShanBin MEI Hu ZHOU Yuan yanG Li ZHOU peng yanG ShengXi SHU Mao LIAO Chunyang WU ShiRong LI GenRong HE liu GAO jianKun Gan MengYu LI DeJing CHEN guoping WANG GuiXue LONG Sha JING JuHua ZHENG XiaoLin ZENG Hui ZHANG QiaoXia ZHANG MengJun yanG Qi TIAN FeiFei TONG jianBo WANG JiaoNa liu YongHong LI Bo QIU LiangJia CAI ShaoXi ZHAO Na yanG yan SU XiaLi SONG jian CHEN MeiXia ZHANG XueJiao SUN JiaYing LI JingWei CHEN GuoHua CHEN Gang DENG Jie peng ChuanYou ZHU WanPing XU LuoNan WU YuQuan LIAO LiMin LI Zhi LI Jun LU DaJun SU QinLiang HUANG ZhengHu ZHOU Ping LI ZhiLiang College of Chemistry and Chemical Engineering Chongqing UniversityChongqing 400044China Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education at Chongqing University Chongqing 400044China College of Bioengineering Chongqing UniversityChongqing 400044China Technology Centre for Life Sciences and Biotechnology Singapore PolytechnicSingapore 139651Singapore

Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.

关键词： avian influenza virus (AIV) HA protein support vector machine (SVM) linear discriminant analysis (LDA)

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