基于深度卷积神经网络的低剂量CT肺部去噪
|
摘要
为了降低低剂量CT肺部噪声对肺癌筛查后期诊断的影响,该文提出一种基于深度卷积神经网络的低剂量CT肺部去噪算法。以完整的CT肺部图像作为输入,池化层对输入图像进行降维处理;批规范化解决随着网络深度的增加性能降低的问题;引入残差学习,学习模型中每一层的残差,最后输出去噪图像。与经典去噪算法实验结果对比,所提方法在解决去噪方面达到了很好的滤波效果,同时也较好地保留了肺部图像的细节信息,大大优于传统的去噪算法。
In order to reduce the effect of low dose CT lung noise on the late diagnosis of lung cancer screening,a denoising model of low-dose CT lung based on deep convolution neural network is proposed.The input of the model is the complete CT lung image.The pooling layer reduces the dimension of input.Batch normalization works out the poor performance with the increase of network depth.The residuals of each layer are learned with residual learning.Finally,the denoised image is produced.Compared with classical methods,the proposed method achieves good filtering effect in solving the denoising method,and also retaining the details of lung image information,which is much better than the traditional filtering algorithm.
In order to reduce the effect of low dose CT lung noise on the late diagnosis of lung cancer screening,a denoising model of low-dose CT lung based on deep convolution neural network is proposed.The input of the model is the complete CT lung image.The pooling layer reduces the dimension of input.Batch normalization works out the poor performance with the increase of network depth.The residuals of each layer are learned with residual learning.Finally,the denoised image is produced.Compared with classical methods,the proposed method achieves good filtering effect in solving the denoising method,and also retaining the details of lung image information,which is much better than the traditional filtering algorithm.
引文
[1]张云逸.低剂量CT图像的质量改善算法研究[D].[硕士论文],郑州大学,2015.ZHANG Yunyi.Study of image quality improvement algorithm for low-dose CT[D].[Master dissertation],Zhengzhou University,2015.
[2]刘光达,赵立荣.基于最小均方误差原理的医学X光影像滤波阈值选择[J].光学精密工程,2001,9(1):47-50.LIU Guangda and ZHAO Lirong.Threshold selection method for medical X-ray images filter based on minimum evensquare error[J].Optics and Precision Engineering,2001,9(1):47-50.
[3]SPERL J,BEQUE D,CLAUS B,et al.Computer-Assisted Scan Protocol and Reconstruction(CASPAR)-reduction of image noise and patient dose[J].IEEE Transactions on Medical Imaging,2010,29(3):724-732.doi:10.1109/TMI.2009.2034515.
[4]代双凤,吕科,翟锐,等.基于3D区域增长法和改进的凸包算法相结合的全肺分割方法[J].电子与信息学报,2016,38(9):2358-2364.doi:10.11999/JEIT151365.DAI Shuangfeng,LüKe,ZHAI Rui,et al.Lung segmentation method based on 3D region growing method and improved convex hull algorithm[J].Journal of Electronics&Information Technology,2016,38(9):2358-2364.doi:10.11999/JEIT151365.
[5]徐玉风.基于贝叶斯估计的低剂量CT图像去噪算法[D].[硕士论文],郑州大学,2016.XU Yufeng.The denoising algorithm of low-dose CT images based on bayesian estimation[D].[Master dissertation],Zhengzhou University,2016.
[6]GREEN M,MAROM E M,KIRYATI N,et al.Efficient LowDose CT Denoising by Locally-Consistent Non-Local Means(LC-NLM)[M].Heidelberg:Springer,2016:423-431.doi:10.1007/978-3-319-46729-9_49.
[7]SHANG Xiaobao,DING Yong,DENG Ruizhe,et al.A threedimensional denoising method for low-dose computed tomography[J].Journal of Medical Imaging&Health Informatics,2017,7(1):283-287.doi:10.1166/jmihi.2017.2020.
[8]毕一鸣.低剂量CT图像恢复算法研究[D].[硕士论文],南方医科大学,2010:12-60.BI Yiming.Image restoration for low-dose computed tomography[D].[Master dissertation],Southern Medical University,2010:12-60.
[9]ZHANG H,MA J,WANG J,et al.Statistical image reconstruction for low-dose CT using nonlocal means-based regularization.Part II:An adaptive approach[J].Computerized Medical Imaging&Graphics the Official Journal of the Computerized Medical Imaging Society,2015,43(6):26-35.doi:10.1016/j.compmedimag.2015.02.008.
[10]朱永成,陈阳,罗立民,等.基于字典学习的低剂量X-ray CT图像去噪[J].东南大学学报(自然科学版),2012,42(5):864-868.doi:10.3969/j.issn.1001-0505.2012.05.013.ZHU Yongcheng,CHEN Yang,LUO Limin,et al.Dictionary learning based denoising of low-dose X-ray CT image[J].Journal of Southeast University(Natural Science Edition),2012,42(5):864-868.doi:10.3969/j.issn.1001-0505.2012.05.013.
[11]LORE K G,AKINTAYO A,and SARKAR S.LLNet:A deep autoencoder approach to natural low-light image enhancement[J].Pattern Recognition,2015,61(1):650-662.doi:10.1016/j.patcog.2016.06.008.
[12]LI H M.Deep learning for image denoising[J].International Journal of Signal Processing Image Processing&P,2014,3(7):171-180.doi:10.14257/ijsip.2014.7.3.14.
[13]XIE J,XU L,and CHEN E.Image denoising and inpainting with deep neural networks[C].International Conference on Neural Information Processing Systems,Harrahs,2012,1:341-349.
[14]李传朋,秦品乐,张晋京.基于深度卷积神经网络的图像去噪研究[J].计算机工程,2017,43(3):253-260.doi:10.3969/j.issn.1000-3428.2017.03.042.LI Chuanpeng,QIN Pinle,and ZHANG Jinjing.Research on image denoising based on deep convolutional neural network[J].Computer Engineering,2017,43(3):253-260.doi:10.3969/j.issn.1000-3428.2017.03.042.
[15]吕永标,赵建伟,曹飞龙.基于复合卷积神经网络的图像去噪算法[J].模式识别与人工智能,2017,30(2):97-105.doi:10.16451/j.cnki.issn1003-6059.201702001.LüYongbiao,ZHAO Jianwei,and CAO Feilong.Image denoising algorithm based on composite convolutional neural network[J].Pattern Recognition and Artificial Intelligence,2017,30(2):97-105.doi:10.16451/j.cnki.issn1003-6059.201702001.
[16]ZHANG K,ZUO W,CHEN Y,et al.Beyond a Gaussian denoiser:Residual learning of deep CNN for image denoising[J].IEEE Transactions on Image Processing,2017,26(7):3142-3155.doi:10.1109/TIP.2017.2662206.
[17]周飞燕,金林鹏,董军.卷积神经网络研究综述[J].计算机学报,2017,40(6):1229-1251.doi:10.11897/SP.J.1016.2017.01229.ZHOU Feiyan,JIN Linpeng,and DONG Jun.Review of convolutional neural network[J].Chinese Journal of Computers,2017,40(6):1229-1251.doi:10.11897/SP.J.1016.2017.01229.
[18]王华利,邹俊忠,张见,等.基于深度卷积神经网络的快速图像分类算法[J].计算机工程与应用,2017,53(13):181-188.doi:10.3778/j.issn.1002-8331.1601-0435.WANG Huali,ZOU Junzhong,ZHANG Jian,et al.Fast image classification algorithm based on deep convolutional neural network[J].Computer Engineering and Applications,2017,53(13):181-188.doi:10.3778/j.issn.1002-8331.1601-0435.
[19]HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C].IEEE Computer Vision and Pattern Recognition,Las Vegas,2016:770-778.
[20]ARMATO S,MCLENNAN G,MCNITT-GRAY M,et al.WE-B-201B-02:The Lung Image Database Consortium(LIDC)and Image Database Resource Initiative(IDRI):Acompleted public database of CT scans for lung nodule analysis[J].Medical Physics,2010,37(6):3416-3417.doi:10.1118/1.3469350.
[21]HENSCHKE C I,MCCAULEY D I,YANKELEVITZ D F,et al.Early lung cancer action project:A summary of the findings on baseline screening[J].Oncologist,2001,6(2):147-152.doi:10.1634/theoncologist.6-2-147.
[22]HOR A and ZIOU D.Image quality metrics:PSNR vs.SSIM[C].2010 20th IEEE International Conference on Pattern Recognition,Istanbul,2010:2366-2369.doi:10.1109/ICRP.2010.579.
[2]刘光达,赵立荣.基于最小均方误差原理的医学X光影像滤波阈值选择[J].光学精密工程,2001,9(1):47-50.LIU Guangda and ZHAO Lirong.Threshold selection method for medical X-ray images filter based on minimum evensquare error[J].Optics and Precision Engineering,2001,9(1):47-50.
[3]SPERL J,BEQUE D,CLAUS B,et al.Computer-Assisted Scan Protocol and Reconstruction(CASPAR)-reduction of image noise and patient dose[J].IEEE Transactions on Medical Imaging,2010,29(3):724-732.doi:10.1109/TMI.2009.2034515.
[4]代双凤,吕科,翟锐,等.基于3D区域增长法和改进的凸包算法相结合的全肺分割方法[J].电子与信息学报,2016,38(9):2358-2364.doi:10.11999/JEIT151365.DAI Shuangfeng,LüKe,ZHAI Rui,et al.Lung segmentation method based on 3D region growing method and improved convex hull algorithm[J].Journal of Electronics&Information Technology,2016,38(9):2358-2364.doi:10.11999/JEIT151365.
[5]徐玉风.基于贝叶斯估计的低剂量CT图像去噪算法[D].[硕士论文],郑州大学,2016.XU Yufeng.The denoising algorithm of low-dose CT images based on bayesian estimation[D].[Master dissertation],Zhengzhou University,2016.
[6]GREEN M,MAROM E M,KIRYATI N,et al.Efficient LowDose CT Denoising by Locally-Consistent Non-Local Means(LC-NLM)[M].Heidelberg:Springer,2016:423-431.doi:10.1007/978-3-319-46729-9_49.
[7]SHANG Xiaobao,DING Yong,DENG Ruizhe,et al.A threedimensional denoising method for low-dose computed tomography[J].Journal of Medical Imaging&Health Informatics,2017,7(1):283-287.doi:10.1166/jmihi.2017.2020.
[8]毕一鸣.低剂量CT图像恢复算法研究[D].[硕士论文],南方医科大学,2010:12-60.BI Yiming.Image restoration for low-dose computed tomography[D].[Master dissertation],Southern Medical University,2010:12-60.
[9]ZHANG H,MA J,WANG J,et al.Statistical image reconstruction for low-dose CT using nonlocal means-based regularization.Part II:An adaptive approach[J].Computerized Medical Imaging&Graphics the Official Journal of the Computerized Medical Imaging Society,2015,43(6):26-35.doi:10.1016/j.compmedimag.2015.02.008.
[10]朱永成,陈阳,罗立民,等.基于字典学习的低剂量X-ray CT图像去噪[J].东南大学学报(自然科学版),2012,42(5):864-868.doi:10.3969/j.issn.1001-0505.2012.05.013.ZHU Yongcheng,CHEN Yang,LUO Limin,et al.Dictionary learning based denoising of low-dose X-ray CT image[J].Journal of Southeast University(Natural Science Edition),2012,42(5):864-868.doi:10.3969/j.issn.1001-0505.2012.05.013.
[11]LORE K G,AKINTAYO A,and SARKAR S.LLNet:A deep autoencoder approach to natural low-light image enhancement[J].Pattern Recognition,2015,61(1):650-662.doi:10.1016/j.patcog.2016.06.008.
[12]LI H M.Deep learning for image denoising[J].International Journal of Signal Processing Image Processing&P,2014,3(7):171-180.doi:10.14257/ijsip.2014.7.3.14.
[13]XIE J,XU L,and CHEN E.Image denoising and inpainting with deep neural networks[C].International Conference on Neural Information Processing Systems,Harrahs,2012,1:341-349.
[14]李传朋,秦品乐,张晋京.基于深度卷积神经网络的图像去噪研究[J].计算机工程,2017,43(3):253-260.doi:10.3969/j.issn.1000-3428.2017.03.042.LI Chuanpeng,QIN Pinle,and ZHANG Jinjing.Research on image denoising based on deep convolutional neural network[J].Computer Engineering,2017,43(3):253-260.doi:10.3969/j.issn.1000-3428.2017.03.042.
[15]吕永标,赵建伟,曹飞龙.基于复合卷积神经网络的图像去噪算法[J].模式识别与人工智能,2017,30(2):97-105.doi:10.16451/j.cnki.issn1003-6059.201702001.LüYongbiao,ZHAO Jianwei,and CAO Feilong.Image denoising algorithm based on composite convolutional neural network[J].Pattern Recognition and Artificial Intelligence,2017,30(2):97-105.doi:10.16451/j.cnki.issn1003-6059.201702001.
[16]ZHANG K,ZUO W,CHEN Y,et al.Beyond a Gaussian denoiser:Residual learning of deep CNN for image denoising[J].IEEE Transactions on Image Processing,2017,26(7):3142-3155.doi:10.1109/TIP.2017.2662206.
[17]周飞燕,金林鹏,董军.卷积神经网络研究综述[J].计算机学报,2017,40(6):1229-1251.doi:10.11897/SP.J.1016.2017.01229.ZHOU Feiyan,JIN Linpeng,and DONG Jun.Review of convolutional neural network[J].Chinese Journal of Computers,2017,40(6):1229-1251.doi:10.11897/SP.J.1016.2017.01229.
[18]王华利,邹俊忠,张见,等.基于深度卷积神经网络的快速图像分类算法[J].计算机工程与应用,2017,53(13):181-188.doi:10.3778/j.issn.1002-8331.1601-0435.WANG Huali,ZOU Junzhong,ZHANG Jian,et al.Fast image classification algorithm based on deep convolutional neural network[J].Computer Engineering and Applications,2017,53(13):181-188.doi:10.3778/j.issn.1002-8331.1601-0435.
[19]HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C].IEEE Computer Vision and Pattern Recognition,Las Vegas,2016:770-778.
[20]ARMATO S,MCLENNAN G,MCNITT-GRAY M,et al.WE-B-201B-02:The Lung Image Database Consortium(LIDC)and Image Database Resource Initiative(IDRI):Acompleted public database of CT scans for lung nodule analysis[J].Medical Physics,2010,37(6):3416-3417.doi:10.1118/1.3469350.
[21]HENSCHKE C I,MCCAULEY D I,YANKELEVITZ D F,et al.Early lung cancer action project:A summary of the findings on baseline screening[J].Oncologist,2001,6(2):147-152.doi:10.1634/theoncologist.6-2-147.
[22]HOR A and ZIOU D.Image quality metrics:PSNR vs.SSIM[C].2010 20th IEEE International Conference on Pattern Recognition,Istanbul,2010:2366-2369.doi:10.1109/ICRP.2010.579.