Nonlinear Contrast Enhancement of Medical Ultrasonic Image Based on Munsell's Scale

Xiang Nanfei, Wang Tian-fu, Zou Yuanwen, L. Deyu, Lin Jiangli
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Abstract

Because of various reasons, medical ultrasonic image has a lot of noises. But after filtering processing, it will get blurry especially at the boundary of the region. Almost all of the traditional image contrast enhancement approaches can't preserve the gray values and introduce distortion into the image. In this work, we introduce a nonlinear local contrast enhancement method. This method utilizes the Munsell value scale which is based on human visual perception. Munsell value scale partitions the gray scale into ten discrete subintervals. Then inside each subinterval, we consider the mean edge gray value as the point of the segmentation, and construct an appropriate function to mapping. In this way, the contrast of the image has been enhanced and the shades of the gray value are preserved. Experiment proves that it's an effective medical ultrasonic image contrast enhancement approach.
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基于孟塞尔尺度的医学超声图像非线性对比度增强
由于各种原因,医学超声图像存在大量的噪声。但是经过滤波处理后,图像会变得模糊,尤其是在区域的边界处。几乎所有传统的图像对比度增强方法都不能保持图像的灰度值,并且会给图像带来失真。本文介绍了一种非线性局部对比度增强方法。该方法利用了基于人眼视觉感知的蒙塞尔值尺度。孟塞尔值尺度将灰度划分为十个离散的子区间。然后在每个子区间内,将边缘均值灰度值作为分割点,构造相应的函数进行映射。这样既增强了图像的对比度,又保留了灰度值的阴影。实验证明,该方法是一种有效的医用超声图像对比度增强方法。
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