CT&PET Medical Image Fusion Techniques Based On Statistical Criteria

Abstract

Generally, Images Fusion (IF) is utilized to integration several sets of data in the same position. In other words, the process of IF is used to produce images contain more information. Therefore, it is widely used in medical side, due to the reason; the medical images fusion has fine details that play a significant role in medical diagnosis and treatments. Thus, in this research the problem in medical images from side of integration can be solved via using arithmetical (Brovey Transform, Multiplicative Model and Color Normalized Transform) and statistical methods (Local Mean Matching and Local Mean and Variance Matching). Also, the quality of merge images has been evaluated using two types of measures; the first one is based on determine the quality in image edge regions (contrast calculate with number of edge and threshold), the second type based on image region (Mean, Standard Deviation, Signal to Noise Ratio, and Mutual Information). Depending on the results, these methods appeared the best performance of the merge when applied on medical image.