期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2013
卷号：2
期号：11
页码：6014
出版社：S&S Publications
摘要：Medical imaging is a vital component of large number of applications within current clinical settings. Image registration is a fundamental task in medical imaging. It is a process that overlays two or more medical images that are taken from different devices such as MRI, CT, PET and SPECT etc or taken at different angles. Integration of useful data obtained from different images is often req uired for medical diagnosis and procedure that brings spatial alignment among images is known as registration. We address the problem of image registration by adopting discrete cosine transformation (DCT) with a neural-network distance point learning approach. Instead of explicitly specifying the local regularization parameter values, they are regarded as network weights which are then modified through the supply of appropriate training points from the medical image taken. The desired response of the network is in the form of a gray level value estimate of the current pixel using co ntour distance vector (CDV). At last apply Fuzzy logic. Root mean square error (RMSE) and peak signal to noise ratio (PSNR) are used for accuracy evaluation.
关键词：Magnetic resonance imaging (MRI); Computed Tomography (CT); Positron emission tomography (PET); ; Single positron emission co mputed tomography (SPECT); Discrete cosine transformation (DCT) and Contour distance ; vector (CDV).
