Multimodality Image Fusion by Using Activity Level Measurement and Counterlet Transform

Abstract

In this paper we propose multimodality Medical Image Fusion based on counterlet transform . The objective of image fusion is combine two images to produce single image that provide more information. In this paper, we propose multim odality Image Fusion (MIF) method, this is done on activity level measurement contourlet transform. Multimodality image fusion technology can be used in medical field by doctors to diagnose the disease. Main issue in multimodality image fusion is how to fuse two or more images of different modalities & how we get more clear and accurate information. In this paper to fuse the image firstly we decompose the source image. The low - frequency subbands (LFSs) are fused by using the novel combined ALM (Activity level measurement), and the high - frequency subbands (HFSs) are fused according to their ̳local average energy of the neighborhood of coefficients. Then inverse contourlet transform (ICNT) is used to apply on the fused coefficients to get the fused image. Experimental results demonstrate that the proposed scheme is evaluated by various quantitative measures like Mutual Information, Entropy and Spatial Frequency etc. The purpose of this paper is to replace the wavelet transform with counterlet transform to make image much smoother and to increase the efficiency of the fusion method and quality in the Image.