Multimodal medical image fusion using wavelet transform

Abstract

In the recent years, the study of multimodality medical image fusion attracts much attention with the increasing of clinic application demanding. Radiotherapy plan, for instance, often benefits from the complementary information in images of different modalities. Dose calculation is based on the computed tomography (CT) data, while tumor outlining is often better performed in the corresponding magnetic resonance (MR) scan. For medical diagnosis,CT provides the best information on denser tissue with less distortion, MRI provides better information on soft tissue with more distortion, and PET provides better information on blood fl ow and fl ood activity with low space resolution in general. With more available multimodality medical images in clinical applications, the idea of combining images from different modalities becomes very important and medical image fusion has merged as a new and promising research field. Medical fusion image is to combine functional image and anatomical image together into one image. This paper proposes a global energy fusion strategy which is region level image fusion method. In this paper, match measures are calculated as a whole to select the wavelet coefficients coming from different sub images of the same scale. More abundant information of the edges can be obtained and a more suitable image can be got in view of the human vision.