Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high.

中文翻译：

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基于数据挖掘和机器学习的3D医学图像曲面重建研究

三维（3D）医学图像容易重叠，存在检测效率低、与实际情况不一致等问题。因此，提出了一种基于数据挖掘和机器学习的3D医学图像表面重建方法。将3D医学图像按不同方式分类，建立3D医学图像信息框，给出3D图像表面重叠信息模型。基于该信息框架，构建了3D医学图像重叠区域信息的非线性函数。利用非线性函数的权重计算重叠区域信息的输入输出结果。结合3D医学影像信息的输入方式，设置了信息输出与预期输出之间的误差。利用重叠区域信息的学习率和使用时间对3D医学图像重叠区域信息的非线性函数权重进行修正，通过增加情境项得到重叠信息检测的影响因素，从而完成3D医学图像表面重建信息的检测。实验结果表明，该方法的信息检测结果与实际情况吻合较好，信息检测效率较高。并通过增加情境项得到重叠信息检测的影响因素，从而完成对3D医学图像表面重建信息的检测。实验结果表明，该方法的信息检测结果与实际情况吻合较好，信息检测效率较高。并通过增加情境项得到重叠信息检测的影响因素，从而完成对3D医学图像表面重建信息的检测。实验结果表明，该方法的信息检测结果与实际情况吻合较好，信息检测效率较高。