Fusion of medical images based on salient features extraction by PSO optimized fuzzy logic in NSST domain

The aim of multimodal medical image fusion is to extract information from different modal images into a single one so that the single fusion image contains the salient features of the source images to the maximum extent. Different medical imaging modalities such as CT and MRI are presented as different visual morphology, which often show different complementary salient features in clinical diagnosis. According to this characteristic, in order to fuse different salient features of multimodal medical images into a single image and provide a new dimension of information for clinical diagnosis to improve the diagnosis accuracy, an improved image fusion algorithm based on visual salience detection is proposed in this paper. The visual salience of two registered source images is calculated by the GBVS algorithm, and the low-frequency and high-frequency sub-bands are obtained by decomposing the source images in NSST domain. For the low-frequency sub-bands, fuzzy logic system is used to obtain the respective weights of the fused low-frequency sub-band with the local energy and GBVS graph as input. For the high-frequency sub-bands, the NSML values of each sub-band are calculated and compared to obtain the fused high-frequency sub-band. The final fused image is obtained by the inverse NSST transformation. In addition, PSO algorithm is used to optimize the membership function of fuzzy logic system for better adaption to medical images and feature extraction. By applying this multimodal medical image fusion method, the visual quality of the image is effectively improved and the salient features of tissues are preserved well. Experiments on fusion of grayscale, color and ultrasound multimodal medical images show that the proposed method has advantages on retention of image salient features. Compared with other models, the fused image obtained by the proposed method has higher objective indexes with clearer edge contour, higher overall contrast, and no ringing effect and artifacts.

Automated summary

An improved image fusion algorithm based on visual salience detection is proposed in this study to fuse the salient features of different modal medical images into a single image, effectively improving the visual quality of the image and preserving the salient features of tissues. By applying this multimodal medical image fusion method, the proposed method shows advantages in retaining image salient features with higher objective indexes, clearer edge contour, higher overall contrast, and no ringing effect and artifacts compared to other models.