Biomedical Image Enhancement, Segmentation and Classification Using Wavelet Transform
نویسنده
چکیده
Mathematical methods used for analysis of biomedical data include many topics of interdisciplinary general area of digital signal and image processing and they cover algorithmic tools for image enhancement, image components detection and their segmentation using feature vectors estimated either in the space and frequency domains by selected statistical methods and functional transforms. The paper is devoted to specific topics of biomedical image processing based upon the time-scale image decomposition using the set of dilated and translated wavelet functions. Topics covered include (i) the use of wavelet transform for modification of image resolution, (ii) wavelet coefficients thresholding used for image de-nosing, (iii) evaluation of image components features for their classification into the given number of classes using neural networks. Methods proposed are applied for biomedical images to allow another view to their analysis and to contribute to early diagnostics of serious diseases. Key–Words: Biomedical data processing, wavelet transform, resolution enhancement, contour detection, texture analysis, data de-noising, image components analysis, classification, cluster analysis, neural networks
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