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Research of Medical Image Processing Based on Wavelet Analysis

Author: YangJing
Tutor: ZhangSiJie
School: Chongqing University
Course: Signal and Information Processing
Keywords: Wavelet Transform New threshold function NeighShrink threshold denoising Subband Enhancement Laplacian enhanced
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 129
Quote: 1
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Abstract


With the development of science and technology, medical imaging technology plays an increasingly important role in clinical diagnosis and treatment. Medical images (such as CT, B ultrasound, MRI, etc.) to give doctors a more objective data can be found early surgical treatment of diseases, assist doctors. However, medical images has many flaws, the boundary is not obvious or obscure, the addition image in the imaging process because of the limitations of the imaging mechanism of the device, the interference of the transmission or display devices, and other factors, the quality will be affected to some degree, so that The human eye is difficult to make accurate judgments resulting image, bring some difficulties to the doctor's diagnosis and treatment. In order to improve the readability of medical images, the effective observation of the disease, the correct diagnosis and treatment, medical image processing. The conventional image processing method is easy to amplify noise lost edge or details, and is not suitable for the processing of the medical image. Wavelet analysis is a new time-frequency analysis tool following the Fourier analysis, showing a unique advantage in medical image processing, because it has good time-frequency characteristics of the local features and multi-resolution analysis can achieve a good signal-to-noise separation . This paper studies the medical image denoising based on wavelet transform and enhancement algorithms, mainly to do the following: First, the introduction of several major medical imaging archiving and communication system (PACS) module. The basic theory of wavelet transform image decomposition and reconstruction, and to provide a theoretical basis for the subsequent image processing. Second, the analysis of the wavelet transform modulus maxima de-noising correlation denoising threshold shrinkage denoising propose a new threshold function for the shortcomings of the hard and soft threshold function, and experimental results demonstrate this new threshold function effect compared with the hard and soft threshold function. Further analysis NeighShrink threshold denoising denoising in the wavelet coefficients processing Because NeighShrink threshold, considering the neighborhood of wavelet coefficients to reduce the loss of important details, the better to retain the image edges and details, and to overcome the soft and hard threshold denoising shortcomings. However, it denoising while all of the wavelet coefficients of contraction, the edges of the image or the details have been weakened, and the improvement of the method proposed for this problem, while denoising can enhance the edges of the image, details . Simulation results show that the improved algorithm enforced than wavelet denoising, wavelet soft threshold denoising wavelet hard threshold denoising, NeighShrink threshold denoising effect. Finally, the study highlight image enhancement method based on wavelet transform, wavelet high frequency subband enhancement algorithm based on wavelet enhancement algorithm based on new threshold function and wavelet enhanced image airspace Laplacian enhancement, the small wave Laplacian enhanced method proved by experiments only highlights the edge detail of the image characteristics, suppressing noise amplification effect than a single method.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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