School of Electronic Information Engineering, Ningbo Polytechnic, Ningbo 315800, China.
School of Information and Engineering, China Jiliang University, Hangzhou 310000, Zhejiang, China.
Comput Intell Neurosci. 2022 Jun 13;2022:3500592. doi: 10.1155/2022/3500592. eCollection 2022.
In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized.
在医学图像处理领域，由于组织、器官和成像方法的差异，所获得的医学图像存在显著差异。随着医学智能化的发展，越来越多的基于人工智能技术的计算优化算法也被应用于医学领域。由于基于半监督自训练算法的图像分割算法解决了传统基于聚类的图像分割算法中初始化类中心随机性较大的问题，本文旨在将人工智能半监督自训练算法应用于病理组织图像分割问题中。设计了一个实验组来收集样本图像，并使用本文提出的算法进行图像分割，以获得更好的视觉体验和图像。虽然没有一般的图像分割理论，但近年来许多学者致力于将新概念和新方法应用于图像分割，并将具体的理论图像分割方法结合起来，在图像分割中取得了良好的应用效果。例如，小波分析、小波变换、神经网络和遗传算法可以有效地提高分割效果。本文设计的 Seg 切割方法的结果表明，在对健康人群数据库中的视网膜血管分割结果中，灵敏度值为 0.941633，假阳性率为 0.952933，特异性为 0.956787，准确率为 0.96182，均高于其他方法。FNN、CNN 和 AWN 等图像切割方法已经解决了组织图像切割问题。使用本文设计的 Seg 切割方法对糖尿病患者数据库中的视网膜血管进行分割，灵敏度值为 0.8106，假阳性率为 0.0511，特异性为 0.9712，准确率为 0.9421，省略了假阳性率。假阳性率低于 AWN，其他指标均高于 FNN、CNN、AWN 等图像切割方法。实现了基于人工智能的半监督自训练算法在病理组织图像分割中的应用。
