基于牙科X光片的多种直方图均衡化图像增强对比
|
摘要
牙科X光片在牙科疾病的诊断治疗中越来越重要。牙齿X光片机不像光学相机可以聚焦,常受低对比度和噪声的影响,很难直接借此识别疾病特征,常常需要对图片进行增强处理,故主要探讨一些适用于电子病例的牙科X光图像增强算法。算法主要有直方图均衡化、限制对比度自适应直方图均衡化、锐化中值滤波自适应直方图均衡化和改进的对比度自适应直方图均衡化方法。通过对这些算法处理后的牙科X光片图像的均方根误差(RMSE)、图像信噪比(SNR)和改善对比度指数(CII)以及视觉效果进行比较,发现用改进的对比度自适应直方图均衡化方法处理所得图像的均方根误差、改善对比度指数最小,图像信噪比最大,图像视觉效果清晰。
Dental X-ray photo is more and more important in the diagnosis and treatment of dental diseases.However dental X-ray machine are not like optical camera which can focus.It is often affected by the low contrast and noise and is difficult to indentify the disease characteristics.It needs image enhancement processing.Dental X optical image enhancement algorithm in this paper is mainly about some applicable to the electronic case.This paper discusses the algorithm of histogram equalization and contrast limited adaptive histogram equalization,sharpening,median filtering and adaptive histogram equalization and enhance contrast adaptive histogram equalization method(SMCLAHE). By comparing these algorithms for the dental X-ray image,enhancement effect with root mean square error(RMSE),image signal-to-noise ratio(SNR),improve contrast index(CII)and visual effect comparison are analyzed.It found that SMCLAHE algorithm processing have the smallest RMSE and CII,the largest SNR,and clear view.
Dental X-ray photo is more and more important in the diagnosis and treatment of dental diseases.However dental X-ray machine are not like optical camera which can focus.It is often affected by the low contrast and noise and is difficult to indentify the disease characteristics.It needs image enhancement processing.Dental X optical image enhancement algorithm in this paper is mainly about some applicable to the electronic case.This paper discusses the algorithm of histogram equalization and contrast limited adaptive histogram equalization,sharpening,median filtering and adaptive histogram equalization and enhance contrast adaptive histogram equalization method(SMCLAHE). By comparing these algorithms for the dental X-ray image,enhancement effect with root mean square error(RMSE),image signal-to-noise ratio(SNR),improve contrast index(CII)and visual effect comparison are analyzed.It found that SMCLAHE algorithm processing have the smallest RMSE and CII,the largest SNR,and clear view.
引文
[1]李开端,李树军.基于直方图统计学的图像增强算法研究[J].科学技术与工程,2011,11(23):5572-5575.
[2]张玲.医学图像处理中的小波变换应用[J].中国医学影像技术,2010,26(2):372-374.
[3]刘燕君,刘奇.基于同态滤波与直方图均衡化的超声图像增强[J].中国组织工程研究与临床康复,2011,15(48):9031-9034.
[4]GONZALEZ R C,WOODS R E,EDDINS S L.数字图像处理:MATLAB版[M].阮秋琦,译.北京:电子工业出版社,2005.
[5]姜柏军,钟明霞.改进的直方图均衡化算法在图像增强中的应用[J].激光与红外,2014,44(6):702-706.
[6]HUANG L D,ZHAO W,WANG J,et al.Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement[J].IET Image Processing,2015,9(10):908-915.
[7]张卫国.基于几种典型的图像增强方法实现探讨[J].煤炭技术,2012,31(10):256-257.
[8]孙增国,韩崇昭.基于Laplacian算子的图像增强[J].计算机应用研究,2007,24(1):222-223,240.
[9]卢允伟,陈友荣.基于拉普拉斯算法的图像锐化算法研究和实现[J].电脑知识与技术,2009,5(6):1513-1515.
[10]张旭明,徐滨士,董世运.用于图像处理的自适应中值滤波[J].计算机辅助设计与图形学学报,2005,17(2):295-299.
[11]周杰.应用于图像处理的中值滤波改进算法[D].北京:北京邮电大学,2007.
[12]段影影.医学图像质量评价方法研究[D].广州:南方医科大学,2010.
[13]SAJEEV S,BAJGER M,LEE G.segmentation of breast masses in local dense background using adaptive clip limitCLAHE[C]∥Proceedings of the 2015 international conference on digital image computing:techniques and applications(DICTA).Adelaide,SA:IEEE,2015:1-8.
[14]王俊平,李锦.图像对比度增强研究的进展[J].电子科技,2013,26(5):160-165.
[15]刘立成.医学图像增强若干算法的改进研究[D].长沙:湖南大学,2012.
[2]张玲.医学图像处理中的小波变换应用[J].中国医学影像技术,2010,26(2):372-374.
[3]刘燕君,刘奇.基于同态滤波与直方图均衡化的超声图像增强[J].中国组织工程研究与临床康复,2011,15(48):9031-9034.
[4]GONZALEZ R C,WOODS R E,EDDINS S L.数字图像处理:MATLAB版[M].阮秋琦,译.北京:电子工业出版社,2005.
[5]姜柏军,钟明霞.改进的直方图均衡化算法在图像增强中的应用[J].激光与红外,2014,44(6):702-706.
[6]HUANG L D,ZHAO W,WANG J,et al.Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement[J].IET Image Processing,2015,9(10):908-915.
[7]张卫国.基于几种典型的图像增强方法实现探讨[J].煤炭技术,2012,31(10):256-257.
[8]孙增国,韩崇昭.基于Laplacian算子的图像增强[J].计算机应用研究,2007,24(1):222-223,240.
[9]卢允伟,陈友荣.基于拉普拉斯算法的图像锐化算法研究和实现[J].电脑知识与技术,2009,5(6):1513-1515.
[10]张旭明,徐滨士,董世运.用于图像处理的自适应中值滤波[J].计算机辅助设计与图形学学报,2005,17(2):295-299.
[11]周杰.应用于图像处理的中值滤波改进算法[D].北京:北京邮电大学,2007.
[12]段影影.医学图像质量评价方法研究[D].广州:南方医科大学,2010.
[13]SAJEEV S,BAJGER M,LEE G.segmentation of breast masses in local dense background using adaptive clip limitCLAHE[C]∥Proceedings of the 2015 international conference on digital image computing:techniques and applications(DICTA).Adelaide,SA:IEEE,2015:1-8.
[14]王俊平,李锦.图像对比度增强研究的进展[J].电子科技,2013,26(5):160-165.
[15]刘立成.医学图像增强若干算法的改进研究[D].长沙:湖南大学,2012.