基于多尺度离散Shearlet变换的医学超声图像阈值增强

Deep Gupta, R. Anand, B. Tyagi
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引用次数: 10

摘要

特征保留增强是医学超声图像研究的热点。斑点是影响超声图像质量、对比度分辨率和最重要的纹理信息的主要因素,并且会给后期处理带来困难。提出了一种基于离散剪切波变换和阈值分割的图像增强方法。采用具有各向异性、局域性、方向性和多尺度特征的新型高效多尺度几何表示方法DST对噪声系数进行有效表示。为了提高超声图像的视觉增强效果,考虑到减少与散斑减少相关的模糊和适当地增强/保留细节,对噪声DST系数采用阈值分割方案,以提高去噪效率并有效地保留边缘特征。该算法还有助于提高超声图像的视觉质量。实验结果表明,该方法能够有效地抑制噪声,并根据不同的性能指标来定义特征和边缘。
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Enhancement of Medical Ultrasound Images Using Multiscale Discrete Shearlet Transform Based Thresholding
Feature preserved enhancement is of great interest in medical ultrasound images. Speckle is a main factor which affects the quality, contrast resolution and most importantly texture information present in ultrasound images and can make the post-processing difficult. This paper presents a new enhancement approach which is based on discrete shearlet transform (DST) and thresholding scheme. The DST, a new efficient multiscale geometric representation with the different features of anisotropy, localization, directionality and multiscale, is employed to provide effective representation of the noisy coefficients. Thresholding schemes are applied to the noisy DST coefficients to improve the denoising efficiency and preserve the edge features effectively with this consideration that blurring associated with speckle reduction should be less and fine details are enhanced/preserved properly for the visual enhancement of ultrasound images. The presented algorithm also helps to improve the visual quality of the ultrasound images. Experimental results demonstrate the ability of proposed method for noise suppression, feature and edge preservation defined in terms of different performance measures.
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