摘要
为了改善彩色眼底图像增强后颜色失真及采集过程所造成的对比度低、细节信息模糊甚至细节丢失的问题,提出一种兼顾颜色保真、亮度增强和细节增强相匹配的眼底图像增强方法。首先将RGB空间的眼底图像转换到HSV空间进行基于亮度的自适应幂律变换,以解决眼底图像亮度的平衡问题。其次,在Lab颜色空间中采用受限直方图均衡化方法提高眼底图像的细节信息,使得处理后的彩色眼底图像颜色保真性较好。最后,在DIARETDB0眼底图像数据库上进行分析,并与目前常用的眼底图像增强方法进行对比。对比结果表明,该算法相对于其他算法具有更好的视觉效果,改善了彩色眼底图像的颜色失真及对比度低的问题,客观评价指标优于传统的CLAHE方法,色彩浓度综合指标提高了37.6%,梯度指标提高了54.23%,图像质量评价指标提高了7%,有效提高了眼底图像的质量,为后期眼底图像的识别、分割和分类提供了一种新的预处理方法。
A color fundus image enhancement algorithm is proposed,which it is considering the matching of hue preserving,brightness enhancement and detail enhancement to deal with the problems of poor contrast,low brightness,and color distortion after other image enhancement methods.Firstly,the fundus image in RGB space is converted to HSV space for adaptive power-law transformation based on brightness to solve the problem of brightness balance of fundus image.Secondly,the limited contrast adaptive histogram equalization method is used in Lab color space to improve the detail information of fundus image.Finally,experiments are carried out on Diaretdb0 fundus image database and compared with other enhancement algorithms.The experimental results show that this method has better visual effect than other image enhancement algorithms,improves low contrast and the color distortion of color fundus images.The underwater color image quality evaluation was increased by 37.6%,average gradient was increased by 54.23%,visual parameter measurement index was increased by 7%.It provides a new pre-processing method for the recognition,segmentation and classification of fundus images.
作者
刘玉红
李梓妍
王欣
颜红梅
LIU Yuhong;LI Ziyan;WANG Xin;YAN Hongmei(Department of Biomedical Engineering,Chengdu Medical College,Chengdu 610500;School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu 610054)
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2022年第2期290-294,共5页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(82001906,81801745)
四川省科技厅项目(21YYJC0406)。
关键词
彩色眼底图像
颜色保真
图像增强
图像质量评价
color fundus image
hue preserving
image enhancement
image quality assessment