Medical Image Fusion Based on SWT and Cross Bilateral Filter

Gowhar, Shaik; Haribabu, Maruturi; Guruviah, Velmathi

doi:10.1007/978-981-16-4625-6_89

Cited by 1 publication

(2 citation statements)

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“…18 Gowhar et al fused low coefficients through the cross bilateral filter to improve the entropy contained in the fused medical image. 19 Farbman et al found that the bilateral filter-based decomposition technique has limitations in extracting details of arbitrary scales, and proposed an optimization framework based on weighted least squares, 20 which can effectively preserve edges. 21 In recent years, more complex filters have been proposed, such as the gradient minimization smoothing filter, 22 the segment graph filter, 23 and the hybrid filters.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al used the rolling guided filter to decompose the medical images to suppress artifacts in the results 18 . Gowhar et al fused low coefficients through the cross bilateral filter to improve the entropy contained in the fused medical image 19 . Farbman et al found that the bilateral filter‐based decomposition technique has limitations in extracting details of arbitrary scales, and proposed an optimization framework based on weighted least squares, 20 which can effectively preserve edges 21 .…”

Section: Introductionmentioning

confidence: 99%

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A multimodal molecular image fusion method based on relative total variation and co‐saliency detection

Wang

Zhang

Zhu

2022

Int J Imaging Syst Tech

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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.

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