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An introduction to support Vector Machines: and other kernel-based learning methods
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MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Digital Image Processing
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MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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APVis '04 Proceedings of the 2004 Australasian symposium on Information Visualisation - Volume 35
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VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
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Proceedings of the 8th annual conference on Genetic and evolutionary computation
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Kernel Principal Component Analysis (KPCA) is one of the methods available for analyzing ultrasound medical images of liver cancer. First the original ultrasound images need airspace filtering, frequency filtering and morphologic operation to form the characteristic images and these characteristic images are fused into a new characteristic matrix. Then analyzing the matrix by using KPCA and the principle components (in general, they are not unique) are found in order to that the most general characteristics of the original image can be preserved accurately. Finally the eigenvector projection matrix of the original image which is composed of the principle components can reflect the most essential characteristics of the original images. The simulation experiments were made and effective results were acquired. Compared with the experiments of wavelets, the experiment of KPCA showed that KPCA is more effective than wavelets especially in the application of ultrasound medical images.