Pratap Turimerla, Kokil Priyanka
Department of Electronics and Communication Engineering, Indian Institute of Information Technology Design and Manufacturing, Kancheepuram, Chennai 600127, India.
Department of Electronics and Communication Engineering, Indian Institute of Information Technology Design and Manufacturing, Kancheepuram, Chennai 600127, India.
Comput Methods Programs Biomed. 2021 Mar;200:105927. doi: 10.1016/j.cmpb.2021.105927. Epub 2021 Jan 9.
Computer-aided cataract diagnosis (CACD) methods play a crucial role in early detection of cataract. The existing CACD methods are suffering from performance diminution due to the presence of noise in digital fundus retinal images. The lack of robustness in CACD methods against noise is a serious concern since even the presence of small noise levels may degrade the performance of cataract detection. However, noise in fundus retinal images is unavoidable due to various processes involved in the acquisition or transmission. Hence, a robust CACD method against noisy conditions is required to diagnose the cataract accurately.
In this paper, an efficient network selection based robust CACD method under additive white Gaussian noise (AWGN) is proposed. The presented method consists a set of locally- and globally-trained independent support vector networks with features extracted at various noise levels. A suitable network is then selected based on the noise level present in the input image. The automatic feature extraction technique using pre-trained convolutional neural network (CNN) is adopted to extract features from input fundus retinal images.
A good-quality fundus retinal image dataset is obtained from EyePACS dataset with the use of natural image quality evaluator (NIQE) score. The synthetic noisy fundus retinal images are then generated artificially from good-quality fundus retinal images using AWGN model for effective analysis. The analysis is carried out with existing CNN based CACD methods at different noise levels. From results it is obvious that the proposed CACD method is superior in exhibiting robust performance against AWGN than existing CNN based CACD methods.
From the experimental results, it is clear that the proposed method show superior performance against noise when compared with existing methods in literature. The proposed method can be useful as a starting point to continue further research on CNN based robust CACD methods.
计算机辅助白内障诊断（CACD）方法在白内障的早期检测中起着至关重要的作用。由于数字眼底视网膜图像中存在噪声，现有的CACD方法性能有所下降。CACD方法对噪声缺乏鲁棒性是一个严重问题，因为即使存在小的噪声水平也可能降低白内障检测的性能。然而，由于采集或传输过程中涉及的各种因素，眼底视网膜图像中的噪声是不可避免的。因此，需要一种针对噪声条件的鲁棒CACD方法来准确诊断白内障。
本文提出了一种基于有效网络选择的、在加性高斯白噪声（AWGN）下的鲁棒CACD方法。该方法由一组局部和全局训练的独立支持向量网络组成，这些网络在不同噪声水平下提取特征。然后根据输入图像中存在的噪声水平选择合适的网络。采用基于预训练卷积神经网络（CNN）的自动特征提取技术从输入的眼底视网膜图像中提取特征。
利用自然图像质量评估器（NIQE）得分从EyePACS数据集中获得了高质量的眼底视网膜图像数据集。然后使用AWGN模型从高质量的眼底视网膜图像中人工生成合成噪声眼底视网膜图像，以进行有效分析。在不同噪声水平下，使用现有的基于CNN的CACD方法进行分析。结果表明，所提出的CACD方法在对抗AWGN方面比现有的基于CNN的CACD方法具有更强的鲁棒性能。
从实验结果可以明显看出，与文献中的现有方法相比，所提出的方法在抗噪声方面表现出优越的性能。所提出的方法可作为继续深入研究基于CNN的鲁棒CACD方法的一个起点。
