知识与财富的链接
为提高医学超声图像在临床诊断的效果, 需先对图像进行优化检测和识别, 提出一种基于深度残差网络的医学超声图像多尺度边缘检测算法. 首先, 通过对原始医学超声图像进行自动标注, 构建医学超声图像灰度分布矩阵, 利用分布矩阵完成医学超声图像的多尺度分割; 其次, 构建医学超声图像多尺度边缘的轮廓模型, 提取多尺度图像边缘特征; 再次, 构建深度残差网络结构, 采用深度残差学习算法进行超声图像的底层图像信息融合; 最后, 对融合后的边缘图像数据进行多尺度边缘检测. 实验结果表明, 该算法的图像分割精度高, 特征提取准确率达80%以上, 图像边界中间断区检测效果较好, 边缘点查全性较高, 算法检测耗时短、收敛性强.
In order to improve the effect of medical ultrasonic image in clinical diagnosis, it was necessary to optimize the detection and recognition of images, we proposed a multi-scale edge detection algorithm for medical ultrasonic images based on deep residual network. Firstly, the gray scale distribution matrix of medical ultrasonic image was constructed by automatically tagging the original medical ultrasonic image, and the multi-scale segmentation of medical ultrasonic image was completed by using the distribution matrix. Secondly, the contour model of multi-scale edge of medical ultrasonic image was constructed to extract the edge features of multi-scale image. Thirdly, the deep residual network structure was constructed, and the deep residual learning algorithm was used to fuse the underlying image information of ultrasonic image. Finally, multi-scale edge detection was performed on the fused edge image data. The experimental results show that the proposed algorithm has high accuracy of image segmentation, the accuracy of feature extraction is more than 80%, the detection effect of discontinuous area in the image boundary is good, the edge point checking is high, the detection time of the algorithm is short, and the convergence is strong.