Multimodal Sensor Medical Image Fusion Based on Type-2 Fuzzy Logic in NSCT Domain

Abstract

Multimodal medical image fusion plays a vital role in different clinical imaging sensor applications. This paper presents a novel multimodal medical image fusion method that adopts a multiscale geometric analysis of the nonsubsampled contourlet transform (NSCT) with type-2 fuzzy logic techniques. First, the NSCT was performed on preregistered source images to obtain their high- and low-frequency subbands. Next, an effective type-2 fuzzy logic-based fused rule is proposed for fusion of the high-frequency subbands. In the presented fusion approach, the local type-2 fuzzy entropy is introduced to automatically select high-frequency coefficients. However, for the low-frequency subbands, they were fused by a local energy algorithm based on the corresponding image’s local features. Finally, the fused image was constructed by the inverse NSCT with all composite subbands. Both subjective and objective evaluations showed better contrast, accuracy, and versatility in the proposed approach compared with state-of-the-art methods. Besides, an effective color medical image fusion scheme is also given in this paper that can inhibit color distortion to a large extent and produce an improved visual effect.