卷积神经网络
人工智能
计算机科学
光学相干层析成像
模式识别(心理学)
阈值
膨胀(度量空间)
视网膜
计算机视觉
上下文图像分类
图像(数学)
医学
数学
眼科
组合数学
作者
Yibiao Rong,Dehui Xiang,Weifang Zhu,Kai Yu,Fei Shi,Zhun Fan,Xinjian Chen
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2019-01-01
卷期号:23 (1): 253-263
被引量:86
标识
DOI:10.1109/jbhi.2018.2795545
摘要
Optical Coherence Tomography (OCT) is becoming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly relevant. In this paper, we propose a surrogate-assisted classification method to classify retinal OCT images automatically based on convolutional neural networks (CNNs). Image denoising is first performed to reduce the noise. Thresholding and morphological dilation are applied to extract the masks. The denoised images and the masks are then employed to generate a lot of surrogate images, which are used to train the CNN model. Finally, the prediction for a test image is determined by the average of the outputs from the trained CNN model on the surrogate images. The proposed method has been evaluated on different databases. The results (AUC of 0.9783 in the local database and AUC of 0.9856 in the Duke database) show that the proposed method is a very promising tool for classifying the retinal OCT images automatically.
科研通智能强力驱动
Strongly Powered by AbleSci AI