基于图像分割和全变分的肺CT图像增强  

Lung CT Image Enhancement Based on Image Segmentation and Total Variational

作　　者：王鸿飞 马士青 闵雷 王帅[1,2] 杨伟[5] 许川 杨平[1,2] Wang Hongfei;Ma ShiQing;Min Lei;Wang Shuai;Yang Wei;Xu Chuan;Yang Ping(Key Laboratory of Adaptive Optics,Chinese Academy of Sciences,Chengdu 610209,Sichuan,China;Institute of Optics and Electronics,Chinese Academy of Sciences,Chengdu 610209,Sichuan,China;School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China;Affiliated Cancer Hospital of University of Electronic Science and Technology of China/Sichuan Cancer Hospital,Chengdu 610209,Sichuan,China;School of Aeronautics and Astronautics Xihua University,Chengdu 610209,Sichuan,China)

机构地区：[1]中国科学院自适应光学重点实验室,四川成都610209 [2]中国科学院光电技术研究所,四川成都610209 [3]中国科学院大学电子电气与通信工程学院,北京100049 [4]电子科技大学医学院附属肿瘤医院/四川省肿瘤医院,四川成都610209 [5]西华大学航空航天学院,四川成都610209

出　　处：《中国激光》2022年第20期157-164,共8页Chinese Journal of Lasers

基　　金：国家自然科学基金(61805251,62105336,11704382,62005285);中国科学院“西部青年学者”A类项目。

摘　　要：现有图像增强算法在处理肺部计算机断层扫描(CT)图像时,易产生不自然的外观,引入不必要的人工伪影,并会产生洗去效应。针对此问题,本团队提出了一种基于图像分割和全变分模型的图像增强算法。该算法将图像分割为前景和背景,先对前景肺实质图像的直方图进行修改,然后根据修改的直方图对图像进行伽马拉伸,得到对比度增强的前景图像,再将其与背景图像融合作为全变分模型的输入;然后通过全变分能量泛函将图像分解为纹理层和结构层,对纹理层进行小波阈值去噪,将去噪后的纹理层与结构层进行融合得到增强图像。实验结果的主观分析和客观评价指标均表明,该算法不仅可以有效抑制图像中的伪影噪声,解决现有算法过度增强肺CT图像的问题,还可以充分提高图像的对比度,并保留图像的自然外观显示、纹理细节和边缘特征等信息。Objective In the process of medical image acquisition,due to some factors of the image acquisition device(such as improper parameter adjustment and the limitation of the equipment’s inherent attributes)or the conditions of the object itself(that is,the light absorption and reflection of different attributes)makes the signal collecting process and transferring process in the presence of complicated noise model,causing lung CT image has the characteristics of low contrast and visible mask.Therefore,images with poor visual quality seriously interfere with the efficiency of clinical diagnosis and are a significant obstacle to the subsequent use of images.There is a lot of research on medical image enhancement,but the work on lung CT image enhancement is still lacking.Additionally,when processing images,existing image contrast enhancement algorithms based on histogram equalization tend to introduce unnecessary artifacts,produce an artificial appearance,and cause wash-off effects.Therefore,this paper researched lung CT image enhancement.Methods We devote to overcoming this over-enhancing problem of existing algorithms and then propose an algorithm which can realize appropriate contrast enhancement without introducing new artifacts,that is an image enhancement algorithm based on image segmentation and a total variation model.As is known to all,lung CT images are poor in contrast due to their narrow dynamic grayscale range.And the visual perception of difference relies on gray histogram distribution characteristics to a great extent.Therefore,the research method of contrast enhancement adopted in this article is based on gray histogram transformation.Furthermore,regarding the feature differences between the foreground and background of lung CT images,a segmentation method based on a global threshold is used to segment the lung parenchyma that doctors are interested in for further processing.As for the complex noise model in the image,traditional denoising methods are challenging to ensure the regularization of image en

关 键 词：医用光学 图像增强 图像分割 伽马变换 全变分模型 小波变换 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]