Structure extraction of images using anisotropic diffusion with directional second neighbour derivative operator

Al-nasrawi, Mukhalad; Deng, Guang; Waheed, Waseem

doi:10.1007/s11042-018-6377-7

Cited by 4 publications

(1 citation statement)

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“…Compared with the basic scale-space filters above, the anisotropic diffusion is sensitive to texture, and performs well in preserving edge information. It can separate images into base layers and texture layers better [42], [43]. At the same time, the fusion rule for texture layers is designed using the common coefficients obtained by image decomposition.…”

Section: Introductionmentioning

confidence: 99%

MRI and CT Medical Image Fusion Based on Synchronized-Anisotropic Diffusion Model

Zhu

Zhang

et al. 2020

IEEE Access

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Medical image fusion can combine information from multi-modality images and express them through a single image. How to design a fusion method to preserve more information becomes a hot topic. In this paper, we propose a novel multi-modality medical image fusion method based on Synchronized-Anisotropic Diffusion Equation (S-ADE). First, the modified S-ADE model which is more suitable for Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is employed to decompose two source images. We get the base layers and texture layers. Next, the ''Maximum Absolute Value'' rule is used for base layers fusion. On texture layers, the fusion decision map is calculated by New Sum of Modified Anisotropic Laplacian (NSMAL) algorithm which is designed using common decomposition coefficients by anisotropic diffusion. Furthermore, the consistency check is constructed on the decision map to mitigate the staircase effect. After that, the fused image is obtained by a simple linear combination of layers. Finally, the fused MR/CT image is obtained after image correction. Its aim is to eliminate redundant texture information which is from MRI images in the contour part. The extensive experimental results demonstrate that the proposed method preserves much information as well as guarantees image quality and visual effects. It outperforms other state-of-the-art methods in terms of subjective and objective evaluations. INDEX TERMS Anisotropic diffusion, medical image, multi-modality image fusion, synchronism.

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Section: Introductionmentioning

confidence: 99%