At present, in the application of feature-based medical 3D reconstruction technology, there are still problems such as low matching accuracy of feature points in endoscope images and slow processing speed of image data. Therefore, the feature-based 3D reconstruction theory is of great research value and has great application value. This paper proposed a new feature detection method to improve the problems. This paper divides feature detection into two parts for further improvements: feature extraction and feature description. For feature extraction, the FAST algorithm shows a poor classification effect, so this paper adds the decision tree based on the C4.5 algorithm into the traditional FAST. The original data are divided into two decision trees to make the feature extraction performance more stable and feature point extraction more efficient. For the feature description part, the FREAK descriptor is used, combined with this paper's improved feature extraction algorithm. The feature points are extracted in scale space. The second-order function fitting is carried out according to the feature points' response scores in different scales. The scale-invariant descriptor of sub-pixel precision is obtained. The experimental results on the endoscope image show that the feature extraction method has a higher extraction accuracy and faster extraction speed. In addition, the feature description algorithm has higher calculation efficiency.

目前，基于特征的医学3D重建技术在应用中还存在内窥镜图像特征点匹配精度低、图像数据处理速度慢等问题。因此，基于特征的3D重建理论具有很大的研究价值和应用价值。针对上述问题，本文提出了一种新的特征检测方法。本文将特征检测分为两部分进行进一步改进：特征提取和特征描述。对于特征提取，FAST算法的分类效果较差，因此本文在传统的FAST算法中加入了基于C4.5算法的决策树。将原始数据分成两棵决策树，使特征提取性能更稳定，特征点提取更高效。特征描述部分采用FREAK描述子，结合本文改进的特征提取算法。在尺度空间中提取特征点。根据特征点在不同尺度下的响应分数进行二阶函数拟合。得到亚像素精度的尺度不变描述符。在内窥镜图像上的实验结果表明，该特征提取方法具有更高的提取精度和更快的提取速度。此外，特征描述算法具有更高的计算效率。根据特征点在不同尺度下的响应分数进行二阶函数拟合。得到亚像素精度的尺度不变描述符。在内窥镜图像上的实验结果表明，该特征提取方法具有更高的提取精度和更快的提取速度。此外，特征描述算法具有更高的计算效率。根据特征点在不同尺度下的响应分数进行二阶函数拟合。得到亚像素精度的尺度不变描述符。在内窥镜图像上的实验结果表明，该特征提取方法具有更高的提取精度和更快的提取速度。此外，特征描述算法具有更高的计算效率。