In order to address the problems of time-consuming, low accuracy and poor convergence effect of traditional image automatic annotation algorithm, an Automatic annotation algorithm of medical radiological images based on convolutional neural network (CNN) is proposed. First of all, the image gradient information model was constructed, the edge contour feature of medical radiation image was initialized, the automatic segmentation model of medical radiation image was established by block template matching method, and the automatic segmentation processing of medical radiation image was completed. Secondly, by fusing the contour and gray information of image segmentation, the multi-resolution feature is extracted by using the three-dimensional distributed pixel sequence of image. The fusion feature decomposition of the image was obtained based on CNN, and the automatic annotation of medical radiation image was completed. The results show that the image segmentation effect of the proposed algorithm is good, the number of feature points is accurate, and the accuracy of multi-resolution feature extraction is as high as 98.7%. The convergence of image annotation is good, short time-consumption, and the F1 measurement value of the algorithm is high, and the overall performance is good.

针对传统图像自动标注算法耗时、准确率低、收敛效果差等问题，提出了一种基于卷积神经网络（CNN）的医学放射影像自动标注算法。首先构建图像梯度信息模型，初始化医用放射图像边缘轮廓特征，采用块模板匹配法建立医用放射图像自动分割模型，完成医用放射图像自动分割处理. 其次，融合图像分割的轮廓和灰度信息，利用图像三维分布的像素序列提取多分辨率特征。基于CNN得到图像的融合特征分解，完成了医学放射影像的自动标注。结果表明，该算法图像分割效果好，特征点个数准确，多分辨率特征提取准确率高达98.7%。图像标注收敛性好，耗时短，算法F1测量值高，整体性能好。