基于Multi-Scale Retinex模型的肝脏超声图像增强算法

基于多尺度Retinex (Multi-scale Retinex,MSR)模型的图像增强方法是一种基于人眼视觉原理的图像增强方法,能同时有效实现图像的动态范围压缩和颜色保真.本文融合Retinex理论,采用3个尺度的Retinex算法对肝脏超声图像进行增强处理,提取经MSR算法增强处理后的正常肝脏和脂肪肝图像的灰度均值、对比度和信息熵参数,并与直方图均衡化算法、同态滤波算法进行对比.实验结果表明:肝脏超声图像经MSR算法增强处理后,提高了图像特征参数(对比度、信息熵)的区分度,增强了图像暗区的对比度和清晰度,改善了图像视觉质量,能有效辅助临床诊断.

Enhancement of Ultrasonic Liver Images Based on Multi-Scale Retinex Model

Ultrasound liver images have characteristics such as wide dynamic range, abundant details and low contrast, which are not propitious for features extraction and pattern classification. Images enhancement algorithms based on Multi-Scale Retinex (MSR) combines the dynamic range compression with the tonal rendition at the same time. We apply three-scale Retinex model to enhance ultrasonic liver images, then extract the lightness, contrast, and entropy of the enhanced images, comparing the results with that of the usual image enhancement methods, such as histogram equalization algorithm and homomorphic filtering algorithm. Experiment results show that image enhancement algorithms based on MSR model can improve the distinguish degree of ultrasonic liver images features (contrast, entropy), and increase contrast and entropy, which is helpful to the assisted diagnosis of liver diseases.