Comprehensive Analysis of LPG-PCA Algorithms in Denoising and Deblurring of Medical Images

摘要

This article presents the detailed analysis of the local pixel grouping-principle component analysis (LPG-PCA) algorithm in denoising and deblurring of medical images. Inefficient diagnosis of the medical images containing lot of information is often affected by the noise and artifacts. In order to remove these noises and artifacts, a statistical decorrelation technique, LPG-PCA is used which is found to be one of the efficient methods, which could be used in improving the performance of medical images. For better preservation of local structures of the image, a pixel and its nearest neighbors are modeled as a vector variable, which leads to the selection of similar intensity characteristics. Denoising method used in this article is done in two stages for improving the denoising performance. The smoothening caused by the denoising process is removed by using LPG-PCA along with adaptive sparse domain representations in the deblurring process. This involves clustering of data and finding the subdictionary of each cluster using LPG-PCA. Experimental results show that an average improvement of 2.9 and 5.1 dB is found in the computed tomography and magnetic resonance imaging images using denoising and deblurring process.