The study of the grain-size distribution of gravels is always an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual process...The study of the grain-size distribution of gravels is always an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual processes and time consumption. Precise segmentation method plays a very important role in it. In this study, a digital image method using an improved normalized cuts algorithm is proposed for auto-segmentation of gravel image. It added grain-size estimation, and used the feature vector based on color. It has made great improvements in many respects, especially in accuracy of edge segmentation and automation. Compared with manual measurement methods and other image processing methods, the method studied in this paper is an efficient method for precisely segmenting gravel images.展开更多
<strong>Purpose</strong><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong></span><span style=&q...<strong>Purpose</strong><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong></span><span style="font-family:Verdana;">To improve the liver auto-segmentation performance of three-</span><span style="font-family:Verdana;">dimensional (3D) U-net by replacing the conventional up-sampling convolution layers with the Pixel De-convolutional Network (PDN) that considers spatial features. </span><b><span style="font-family:Verdana;">Methods</span></b><span style="font-family:Verdana;">: The U-net was originally developed to segment neuronal structure with outstanding performance but suffered serious artifacts from indirectly unrelated adjacent pixels in its up-sampling layers. The hypothesis of this study was that the segmentation quality of </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">liver could be improved with PDN in which the up-sampling layer was replaced by a pixel de-convolution layer (PDL). Seventy</span><span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;">eight plans of abdominal cancer patients were anonymized and exported. Sixty-two were chosen for training two networks: 1) 3D U-Net, and 2) 3D PDN, by minimizing the Dice loss function. The other sixteen plans were used to test the performance. The similarity Dice and Average Hausdorff Distance (AHD) were calculated and compared between these two networks. </span><b><span style="font-family:Verdana;">Results</span></b><span style="font-family:Verdana;">: The computation time for 62 training cases and 200 training epochs was about 30 minutes for both networks. The segmentation performance was evaluated using the remaining 16 cases. For the Dice score, the mean ± standard deviation were 0.857 ± 0.011 and 0.858 ± 0.015 for the PDN and U-Net, respectively. For the AHD, the mean ± standard deviation were 1.575 ± 0.373 and 1.675 ± 0.769, respectively, corresponding to an improvement of 6.0% and 51.5% of mean and standard deviation for the PDN. </span><b><span style="font-family:Verdana;">Conclusion</span></b><span style="font-family:Verdana;">: The PDN has outperformed the U-Net on liver auto-segmentation. The predicted contours of PDN are more conformal and smoother when compared with</span></span><span style="font-family:Verdana;"> the</span><span style="font-family:Verdana;"> U-Net.</span>展开更多
文摘The study of the grain-size distribution of gravels is always an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual processes and time consumption. Precise segmentation method plays a very important role in it. In this study, a digital image method using an improved normalized cuts algorithm is proposed for auto-segmentation of gravel image. It added grain-size estimation, and used the feature vector based on color. It has made great improvements in many respects, especially in accuracy of edge segmentation and automation. Compared with manual measurement methods and other image processing methods, the method studied in this paper is an efficient method for precisely segmenting gravel images.
文摘<strong>Purpose</strong><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong></span><span style="font-family:Verdana;">To improve the liver auto-segmentation performance of three-</span><span style="font-family:Verdana;">dimensional (3D) U-net by replacing the conventional up-sampling convolution layers with the Pixel De-convolutional Network (PDN) that considers spatial features. </span><b><span style="font-family:Verdana;">Methods</span></b><span style="font-family:Verdana;">: The U-net was originally developed to segment neuronal structure with outstanding performance but suffered serious artifacts from indirectly unrelated adjacent pixels in its up-sampling layers. The hypothesis of this study was that the segmentation quality of </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">liver could be improved with PDN in which the up-sampling layer was replaced by a pixel de-convolution layer (PDL). Seventy</span><span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;">eight plans of abdominal cancer patients were anonymized and exported. Sixty-two were chosen for training two networks: 1) 3D U-Net, and 2) 3D PDN, by minimizing the Dice loss function. The other sixteen plans were used to test the performance. The similarity Dice and Average Hausdorff Distance (AHD) were calculated and compared between these two networks. </span><b><span style="font-family:Verdana;">Results</span></b><span style="font-family:Verdana;">: The computation time for 62 training cases and 200 training epochs was about 30 minutes for both networks. The segmentation performance was evaluated using the remaining 16 cases. For the Dice score, the mean ± standard deviation were 0.857 ± 0.011 and 0.858 ± 0.015 for the PDN and U-Net, respectively. For the AHD, the mean ± standard deviation were 1.575 ± 0.373 and 1.675 ± 0.769, respectively, corresponding to an improvement of 6.0% and 51.5% of mean and standard deviation for the PDN. </span><b><span style="font-family:Verdana;">Conclusion</span></b><span style="font-family:Verdana;">: The PDN has outperformed the U-Net on liver auto-segmentation. The predicted contours of PDN are more conformal and smoother when compared with</span></span><span style="font-family:Verdana;"> the</span><span style="font-family:Verdana;"> U-Net.</span>
文摘针对黑白电影的上色过程中,自动上色模型只生成一种结果导致上色结果单一、基于参考示例上色方法需要用户指定参考图像、参考图像的高要求会耗费大量人力的问题,提出了一种多阶段的黑白影像智能色彩修复算法(A Multi-Stage Intelligent Color Restoration Algorithm for Black-and-White Movies,MSICRA)。首先,使用VGG19网络将电影分割为多个场景片段;其次,将每个场景片段逐帧切割,将每帧图像的边缘强度和灰度差作为图像清晰度评判指标,筛选出每个场景中清晰度位于[0.95,1]区间的图像;然后,选择筛选出的图像中的第一张,使用不同的渲染因子值进行上色,利用饱和度进行上色效果的评估,选择合适的渲染因子值对筛选出的图像上色;最后,利用上色前和上色后图像之间的均方误差选择上色质量较好的图像作为该场景片段上色的参考图像。实验结果表明,所提算法在黑白电影《雷锋》和《永不消逝的电波》的PSNR上分别提高了1.32%和2.15%,SSIM分别提高了1.84%和1.04%。该算法不仅可以实现全自动上色,而且颜色真实,符合人们的认知。
