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Sharpening enhancement technique for MR images to enhance the segmentation

机译:MR图像的锐化增强技术以增强分割效果

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摘要

HighlightsThis method involves the LPSVD based sharpening enhancement technique.The LP decomposition augments the high-frequency content of the image using shape-invariant properties of edges.The SVD techniques enhance the contrast of edge pixels to bring out the boundary features.The subjective assessment parameters like PSNR, MSSIM, AMBE are used to evaluate the performance of the techniques.AbstractThis study presents a sharpening image enhancement technique based on the Laplacian pyramid (LP) and singular value decomposition (SVD) to improve the visibility and segmentation of subtle organs. The technique utilizes the shape-invariant properties of LP and the SVD techniques to enhance the perceptual sharpness of an image. The sharpening enhancement of magnetic resonance images not only sharpens the edges, but also reduces the noise effect. The results are compared with state-of-art enhancement techniques. The performance measures like peak-signal-to noise (PSNR), mean structural similarity index (MSSIM), and absolute mean brightness error (AMBE) evaluates the improved performance of the proposed technique.
机译: 突出显示 此方法涉及基于LPSVD的锐化增强技术。 LP分解利用边缘的形状不变特性来增强图像的高频内容。 / ce:para> SVD技术增强了边缘像素的对比度,以显示边界特征。 使用PSNR,MSSIM,AMBE等主观评估参数来评估技术的性能。 < / ce:abstract-sec> 摘要 此研究提出了一种基于拉普拉斯金字塔(LP)和奇异值分解(SVD)的锐化图像增强技术),以提高对微妙器官的可见度和分割度。该技术利用LP的形状不变特性和SVD技术来增强图像的感知清晰度。磁共振图像的锐化增强不仅使边缘锐化,而且降低了噪声影响。将结果与最新的增强技术进行比较。峰值信噪比(PSNR),平均结构相似性指数(MSSIM)和绝对平均亮度误差(AMBE)等性能指标评估了该技术的改进性能。

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