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Human visual system based unsharp masking for enhancement of mammographic images

机译:基于人类视觉系统的不清晰蒙版,可增强乳腺X线摄影图像

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Non-Linear Polynomial Filters (NPF) consists of a schema of linear and quadratic filter components operating as a fusion of low-and high pass filters. NPF has shown distinguished performance when applied for mammogram enhancement. The role has been multifaceted, as there is visual contrast improvement of Region-of-Interest (ROI), i.e. the tumor region as well as those of the surrounding diagnostic features. This paper presents the usage of NPF in design of Non-Linear Unsharp Masking (UM) framework for the enhancement of X-ray mammograms (digital mammographic images). The UM approach presented consists of operational modules namely: edge preserving and contrast enhancement algorithms which are realized using different variants of NPF. Application of Human Visual System (HVS) based adaptive thresholding during contrast enhancement provides for an effective minimization of background noises. The responses of the different modules are then combined using non-linear fusion operators based on an improved logarithmic model of perception and human vision. The obtained enhancement results demonstrate noteworthy improvement in contrast of lesion region together with better visualization of lesion margins and fine details. It has been subjectively as well as objectively shown that the enhancement of the contrast and edges do not introduces unwanted overshoots in the ROI. (C) 2016 Elsevier B.V. All rights reserved.
机译:非线性多项式滤波器(NPF)由线性和二次滤波器组件的模式组成,可将低通和高通滤波器融合在一起。 NPF在应用于乳腺X光检查时表现出卓越的性能。由于兴趣区域(ROI)(即肿瘤区域以及周围诊断特征区域)的视觉对比度得到了改善,因此该角色已被多方面化。本文介绍了NPF在设计用于增强X线乳房X线照片(数字乳房X线照片)的非线性Unsharp Masking(UM)框架设计中的用途。提出的UM方法包括以下操作模块:边缘保留和对比度增强算法,这些算法是使用NPF的不同变体实现的。在对比度增强期间基于人类视觉系统(HVS)的自适应阈值的应用可有效降低背景噪音。然后,基于感知和人类视觉的改进对数模型,使用非线性融合算子组合不同模块的响应。获得的增强结果表明,在病变区域的对比度方面,值得注意的改善是对病变边缘和精细细节的更好可视化。主观和客观地表明,对比度和边缘的增强不会在ROI中引入不必要的过冲。 (C)2016 Elsevier B.V.保留所有权利。

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