Recently, the digitalization of images for medical purposes has been progressing, and accordingly there has emerged a wide divergence of views on filtering of images. Filtering has been routinely used in processing images in computed radiography (CR), computed tomography (CT), and magnetic resonance imaging (MRI). Filtering is invaluable because, in processing medical images, both noise and quality should be always considered. The kind of filtering which is presently used often filters out necessary signal data as well, and this can be a serious problem in clinical use.
      A major analyzing method in Wavelet analysis is Multi Resolution Analysis (MRA). As was shown in our previous article, MRA is superior in computational time, precision of processing, freedom of filtering, and so on, so it is very useful as a filtering of such signal data with various modalities as medical image data. Wavelet analysis is superior in detecting a certain signal which is hidden in a noise, and the analysis is expected to have a lot of advantages when it is used to process signal data which often have many noises as medical image data. Then, in this article, we will examine filtering using Wavelet analysis.