Abstract
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.
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Hu, T., Gui, T. (2008). Characteristics Preserving of Ultrasound Medical Images Based on Kernel Principal Component Analysis. In: Gao, X., Müller, H., Loomes, M.J., Comley, R., Luo, S. (eds) Medical Imaging and Informatics. MIMI 2007. Lecture Notes in Computer Science, vol 4987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79490-5_11
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DOI: https://doi.org/10.1007/978-3-540-79490-5_11
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