Multimodal medical image fusion based on saliency features detection in NSST domain

Abstract

Multimodal medical image fusion is to extract information from different modal images into a single one and obtain the organizational characteristics of source images. Different medical imaging measures such as CT and MRI often bring different visual morphology, however, the salient features of tissues are basically the same from the perspective of human eyes. According to this characteristic, an improved image fusion algorithm based on visual salience detection is proposed in this paper. First, the GBVS algorithm was introduced to calculate visual salience of two registered source images, and then decompose the source images in NSST domain to obtain their low-frequency and high-frequency sub-bands. For the low-frequency sub-bands, local energy and GBVS graph are input into fuzzy logic system to obtain the respective weights for the fused low-frequency sub-band. For the high-frequency sub-bands, the NSML values of each sub-band were calculated and compared to obtain the fused high-frequency sub-band. The final fused image was obtained by using the inverse NSST transformation. Applying this method to multimodal medical image fusion, the visual quality of the image can be enhanced effectively and the salient features of tissues can be preserved well. Experiments on multimodal fusion of different gray-scale medical images show that the proposed method has advantages in retention of image salient features and the overall image contrast, and has better objective index than the comparison models.