Multimodal Medical Image Fusion by Adaptive Manifold Filter

Abstract

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.

Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey

“…In addition, a modified spatial frequency is adopted to maintain the high-frequency parts of the modified contrast. The authors suggested that the proposed approach outperforms the approaches in the spatial domain and the guided filter methods with higher mutual information values attained by the presented method 35 .…”