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A Hybrid Multimodal Medical Image Fusion Technique for CT and MRI Brain Images

机译:CT和MRI脑图像的混合多模态医学图像融合技术

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Estimating the type, size, location and spread of a brain tumor is vital in the diagnosis and treatment of brain cancer. Fused CT and MRI brain images assist in faster detection and diagnosis of brain tumors. They provide superior results in comparison to individual CT or MRI images. Multiscale transforms (MSTs) are widely used in fusing multimodal images like CT and MRI. However, they have a few drawbacks like reduced contrast, poor edge detection, redundancy and high computation time. This article describes how MSTs coupled with sparse representation (SR) aims to overcome the drawbacks. Non-Subsampled Contourlet Transform (NSCT) is widely used on MSTs for fusing multifocal images. Therefore, a novel technique using NSCT and SR is proposed for better quality fused CT and MRI images. The experimental results show superior performance.
机译:估计脑肿瘤的类型,大小,位置和扩散在脑癌的诊断和治疗中至关重要。融合的CT和MRI脑图像有助于更快地检测和诊断脑肿瘤。与单独的CT或MRI图像相比,它们提供了更好的结果。多尺度变换(MST)广泛用于融合多模态图像,如CT和MRI。但是,它们具有一些缺点,例如对比度降低,边缘检测不良,冗余和计算时间长。本文介绍了MST与稀疏表示(SR)结合的目的是如何克服这些缺点。非下采样Contourlet变换(NSCT)在MST上广泛用于融合多焦点图像。因此,提出了一种使用NSCT和SR的新技术来获得质量更好的融合CT和MRI图像。实验结果表明性能优越。

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