Technol Health Care. 2021;29(2):363-379. doi: 10.3233/THC-202638.
Medical patients can be diagnosed early, however it is difficult to extract effective features in medical image segmentation based on semantic information.
A deep learning based image pixel block feature learning technology is studied in this paper.
The unlabeled image block sample training stack noise reduction automatic encoder is used to learn and extract the deep features of the image, and construct the initial depth neural network model. The labeled samples are used to fine-tune the initial depth neural network model, the deep features of the image correspond to the category, and the depth neural network model with classification function is obtained. The model is used to classify the pixel block samples in the segmented image and detect the initial segmentation region of brain tumor tissue. Finally, threshold segmentation and morphological methods are used to optimize the initial results to obtain accurate segmentation results of brain tumor tissue.
The results show that this method can effectively improve the accuracy and sensitivity of segmentation. The running speed is also greatly improved compared with the traditional machine learning method.
医学患者可以早期诊断，但基于语义信息的医学图像分割中很难提取有效特征。
本文研究了一种基于深度学习的图像像素块特征学习技术。
使用无标签图像块样本训练堆栈降噪自动编码器来学习和提取图像的深度特征，并构建初始深度神经网络模型。使用有标签样本对初始深度神经网络模型进行微调，使图像的深度特征对应类别，得到具有分类功能的深度神经网络模型。该模型用于对分割图像中的像素块样本进行分类，并检测脑肿瘤组织的初始分割区域。最后，采用阈值分割和形态学方法对初始结果进行优化，得到脑肿瘤组织的准确分割结果。
该方法可以有效地提高分割的准确性和灵敏度。与传统的机器学习方法相比，该方法的运行速度也大大提高。
