Multi-modality medical image fusion based on guided filter and image statistics in multidirectional shearlet transform domain

Abstract

Due to varying imaging principles and complexity of human organ structures, single-modality image can only provide limited information. Multimodality image fusion is the technique which integrates multimodal images into a single image which improves the quality of images by retaining significant features and helps diagnostic imaging practitioners for accurate treatment and evaluation of medical problems. In the current prevailing image fusion techniques presents numerous challenges including the prevelance of fusion artefacts, design complexity and high computational cost. In this paper, a novel multimodal medical image fusion method has been presented to address these problems. The proposed approach is based on combination of guided filter and image statistics in shearlet transform domain. The multimodal images are subjugated to image decomposition using shearlet transform that captures textures information of original images in multidirectional orientations and then decompose these paired images in low-and high-frequency coefficients (i.e. base and detail layers). Then guided filter with high epsilon value is used to obtain weights of original paired images. These weights are then added to the base layer to obtained unified base layers. A guided image filter and image statistics fusion rule is used to fuse base layers to obtain a fused base layer in covariance matrix and Eigen values are computed to figure out the significant pixels in the neighborhood. Similarly, a choose max fusion rule is used to fuse the detail layers for reconstruction. A unified fused base and detail layers are merged together to obtain final fusion result using inverse shearlet transform. The proposed method is evaluated using medical image datasets. Experimental result demonstrates that our proposed algorithm exhibits promising results and outperforms other prevailing fusion techniques.