Affiliations: School of Information Technology, Jiangxi University
of Finance and Economics, Nanchang, China | Division of Electronics and Information Engineering,
Chonbuk National University, Jeonju, Korea | School of Software and Communications Engineering,
Jiangxi University of Finance and Economics, Nanchang, China
Abstract: The fusion of multimodal medical images plays an important role in
many clinical applications as it can support more accurate information than any
individual source image. This paper presents a novel approach for fusion of
computed tomography (CT) and magnetic resonance (MR) images based on wavelet
transform. The medical images to be fused are firstly decomposed into
multiscale representations by the wavelet transform. Then, by considering the
physical meaning of wavelet coefficients and the characteristics of the CT and
MR images, the coefficients of the low frequency band and high frequency bands
are treated with different schemes: the former is performed with a
maximum-selection (MS) rule, and the latter is convolved with a Laplacian
operator followed by a MS rule. Finally, the fused image is reconstructed by
using the inverse wavelet transform with the combined wavelet coefficients. The
performance of our method is qualitatively and quantitatively compared with
some existing fusion approaches. The experimental results can demonstrate that
the proposed method is a promising and effective technique for fusion of CT and
MR images.
Keywords: Multimodal medical images, image fusion, wavelet transform, coefficients
