A GA based Window Selection Methodology to Enhance Window based Multi wavelet transformation and thresholding aided CT image denoising technique

Details A GA based Window Selection Methodology to Enhance Window based Multi wavelet transformation and thresholding aided CT image denoising technique A GA based Window Selection Methodology to Enhance Window based Multi wavelet transformation and thresholding aided CT image denoising technique

2010-03-09

arxiv.org/abs/1003.1826v1

Computer Science

Computer Vision and Pattern Recognition

Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, US

Scientific paper

Image denoising is getting more significance, especially in Computed Tomography (CT), which is an important and most common modality in medical imaging. This is mainly due to that the effectiveness of clinical diagnosis using CT image lies on the image quality. The denoising technique for CT images using window-based Multi-wavelet transformation and thresholding shows the effectiveness in denoising, however, a drawback exists in selecting the closer windows in the process of window-based multi-wavelet transformation and thresholding. Generally, the windows of the duplicate noisy image that are closer to each window of original noisy image are obtained by the checking them sequentially. This leads to the possibility of missing out very closer windows and so enhancement is required in the aforesaid process of the denoising technique. In this paper, we propose a GA-based window selection methodology to include the denoising technique. With the aid of the GA-based window selection methodology, the windows of the duplicate noisy image that are very closer to every window of the original noisy image are extracted in an effective manner. By incorporating the proposed GA-based window selection methodology, the denoising the CT image is performed effectively. Eventually, a comparison is made between the denoising technique with and without the proposed GA-based window selection methodology.

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