Applications of Wavelet Transform in Registration, Segmentation, Denoising, and Compression of Medical Images
Wavelet transforms and other multiscale analysis functions have been used for compact signal and image representations in denoising, compression, and feature detection processing problems. The wavelet transform itself offers great design flexibility. Basis selection, spatial-frequency tiling, and various wavelet threshold strategies can be optimized for best adaptation to a processing application, data characteristics, and feature of interest. Fast implementation of wavelet transforms using a filter-bank framework enables real-time processing capability. Instead of trying to replace standard image processing techniques, wavelet transforms offer an efficient representation of the signal, finely tuned to its intrinsic properties. By combining such representations with simple processing techniques in the transform domain, multiscale analysis can accomplish remarkable performance and efficiency for many image processing problems.
KeywordsWavelet transform using Matlab Image edge detection Segmentation Registration De-noising Lossless image compression Digital imaging and communications in medicine (DICOM) Security issue in transmission Transmission of medical images Measuring lossless compression effectiveness parameters Compression algorithm
I sincerely express indebtedness to my esteemed and revered guide Prof. Mukta Bhatele for her invaluable guidance, supervision, and encouragement throughout the work. I also thank MP, CT scan, and MRI Center, Jabalpur, for its contribution to the collection of images used. This study would not have been possible if compression researchers did not routinely place their code and papers on the Internet for public access.
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