ABSTRACT

For the existing medical image edge detection algorithm image reconstruction accuracy is not high, the fitness of optimization coefficient is low, resulting in the detection results of low information recall, poor smoothness and low detection accuracy, we proposes an edge detection algorithm of cancer image based on deep learning. Firstly, the three-dimensional surface structure reconstruction model of cancer image was constructed. Secondly, the edge contour feature extraction method was used to extract the fine-grained features of cancer cells in the cancer image. Finally, the multi-dimensional pixel feature distributed recombination model of cancer image was constructed, and the fine-grained feature segmentation method was adopted to realize regional fusion and information recombination, and the ultra-fine particle feature was extracted. The adaptive optimization of edge detection was realized by combining with deep learning algorithm. The adaptive optimization in the process of edge detection was realized by combining with the deep learning algorithm. The experimental results show that the three-dimensional reconstruction accuracy of the proposed algorithm is about 95%, the fitness of the optimization coefficient is high, the algorithm has a strong edge information detection ability, and the output result smoothness and the accuracy of edge feature detection are high, which can effectively realize the detection of cancer image edge.

摘要

针对现有医学图像边缘检测算法图像重建精度不高，优化系数适应度低，导致检测结果信息召回率低、平滑度差、检测精度低的问题，提出一种基于癌症图像的边缘检测算法关于深度学习。首先，构建了肿瘤图像三维表面结构重建模型。其次，利用边缘轮廓特征提取方法，提取肿瘤图像中癌细胞的细粒度特征。最后构建肿瘤图像多维像素特征分布式重组模型，采用细粒度特征分割方法实现区域融合和信息重组，提取超细颗粒特征。结合深度学习算法实现边缘检测的自适应优化。结合深度学习算法，实现边缘检测过程中的自适应优化。实验结果表明，该算法的三维重建精度约为95%，优化系数的适应度高，算法具有较强的边缘信息检测能力，输出结果平滑度和边缘特征精度检测率高，可有效实现癌症图像边缘的检测。