A multimodal molecular image fusion method based on relative total variation and co‐saliency detection. Issue 2 (10th October 2022)

• Record Type: Journal Article
• Authors: Wang, Jing; Zhang, Wenjuan; Zhu, Rui
• Is Part Of: International journal of imaging systems and technology. Volume 33:Issue 2(2023)
• Page Start: 523
• Page End: 546
• Publication Date: 2022-10-10
• Subjects: image fusion -- molecular image -- multimodality -- relative total variation -- saliency detection; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22815
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Image fusion can integrate complementary information from multimodal molecular images to provide an informative single result image. In order to obtain a better fusion effect, this article proposes a novel method based on relative total variation and co‐saliency detection (RTVCSD). First, only the gray‐scale anatomical image is decomposed into a base layer and a texture layer according to the relative total variation; then, the three‐channel color functional image is transformed into the luminance and chroma (YUV) color space, and the luminance component Y is directly fused with the base layer of the anatomical image by comparing the co‐saliency information; next, the fused base layer is linearly combined with the texture layer, and the obtained fused result is combined with the chroma information U and V of the functional image. Finally, the fused image is obtained by transforming back to the red–green–blue color space. The dataset consists of magnetic resonance imaging (MRI)/positron emission tomography images, MRI/single photon emission computed tomography (SPECT) images, computed tomography/SPECT images, and green fluorescent protein/phase contrast images, each category with 20 image pairs. Experimental results demonstrate that the proposed method RTVCSD outperforms the nine comparison algorithms in terms of visual effects and objective evaluation. RTVCSD well preserves the texture information of the anatomical image and the metabolism or protein distribution information of the functional image.