CT与PET图像融合的混合算法综述

Gauri D. Patne, P. A. Ghonge, K. Tuckley
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引用次数: 4

摘要

在图像处理中,图像融合用于医学图像的准确诊断。图像融合过程将两幅或多幅图像的信息合并成一幅图像,从而获得信息量很大的图像。本文阐述了使用混合算法对多模态医学图像进行图像融合的概念。身体部位的结构细节,如CT、MRI,以及器官中细胞活动的功能细节,如PET,对分析很重要。因此,这个作品展示了CT和PET图像的融合。离散小波变换(DWT)、平稳小波变换(SWT)、离散曲线变换(DCT)和主成分分析(PCA)是目前应用最广泛的图像融合算法。将传统的融合方法和先进的融合方法相结合,开发了混合算法,克服了各自的缺点,提高了图像处理质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review of CT and PET image fusion using hybrid algorithm
In image processing Image Fusion used in medical images for accuracy of successful diagnosis of disease. Image fusion process gives highly informative image as it combines the information from two or more images into a single image. This paper explains the concept of image fusion using hybrid algorithm for multimodality medical images The structural details of body parts, like CT, MRI, and functional details of cell activity in the organ, like PET are important for analysis. So, this work shows fusion of CT and PET images. Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Discrete Curvelet Transformation (DCT) and Principal Component Analysis (PCA) are most widely used image fusion algorithms. Hybrid algorithm is developed by integrating the conventional and advance fusion methods to overcome their demerits and enhance the image processing qualities The various algorithms are studied, observed and compared the results using the performance MSE, PSNR and ENTROPY.
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