Bio-Medical Imaging Laboratory (BIOMIL), Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, 788010, Assam, India.
Department of Ophthalmology, Silchar Medical College and Hospital, Silchar, 788014, Assam, India.
J Digit Imaging. 2022 Jun;35(3):496-513. doi: 10.1007/s10278-022-00587-x. Epub 2022 Feb 9.
Diabetic retinopathy(DR) is a health condition that affects the retinal blood vessels(BV) and arises in over half of people living with diabetes. Exudates(EX) are significant indications of DR. Early detection and treatment can prevent vision loss in many cases. EX detection is a challenging problem for ophthalmologists due to its different sizes and elevations as retinal fundus images frequently have irregular illumination and are poorly contrasting. Manual detection of EX is a time-consuming process to diagnose a mass number of diabetic patients. In the domain of signal processing, both SIFT (scale-invariant feature transform) and SURF (speed-up robust feature) methods are predominant in scale-invariant location retrieval and have shown a range of advantages. But, when extended to medical images with corresponding weak contrast between reference features and neighboring areas, these methods cannot differentiate significant features. Considering these, in this paper, a novel method is proposed based on modified KAZE features, which is an emerging technique to extract feature points and extreme learning machine autoencoders(ELMAE) for robust and fast localization of the EX in fundus images. The main stages of the proposed method are pre-processing, OD localization, dimensionality reduction using ELMAE, and EX localization. The proposed method is evaluated based on the freely accessible retinal database DIARETDB0, DIARETDB1, e-Ophtha, MESSIDOR, and local retinal database collected from Silchar Medical College and Hospital(SMCH). The sensitivity, specificity, and accuracy obtained by the proposed method are 96.5%, 96.4%, and 97%, respectively, with the processing time of 3.19 seconds per image. The results of this study are satisfactory with state-of-the-art methods. The results indicate that the approach taken can detect EX with less processing time and accurately from the fundus images.
糖尿病性视网膜病变（DR）是一种影响视网膜血管（BV）的健康状况，超过一半的糖尿病患者都会出现这种情况。渗出物（EX）是 DR 的重要指征。在许多情况下，早期发现和治疗可以防止视力丧失。由于眼底图像的光照不均匀且对比度差，EX 的检测对眼科医生来说是一个具有挑战性的问题，其大小和高度都不同。手动检测 EX 是一个耗时的过程，无法诊断大量的糖尿病患者。在信号处理领域，SIFT（尺度不变特征变换）和 SURF（加速稳健特征）方法在尺度不变位置检索中占主导地位，并且具有一系列优势。但是，当将其扩展到参考特征与相邻区域之间对比度较弱的医学图像时，这些方法无法区分显著特征。考虑到这些，本文提出了一种基于改进的 KAZE 特征的新方法，该方法是一种用于提取特征点的新兴技术，以及用于稳健和快速定位眼底图像中 EX 的极端学习机自动编码器（ELMAE）。所提出方法的主要阶段包括预处理、OD 定位、使用 ELMAE 的降维以及 EX 定位。该方法基于可访问的视网膜数据库 DIARETDB0、DIARETDB1、e-Ophtha、MESSIDOR 和从 Silchar 医学院和医院（SMCH）收集的本地视网膜数据库进行评估。该方法的灵敏度、特异性和准确性分别为 96.5%、96.4%和 97%，每张图像的处理时间为 3.19 秒。该研究的结果与最先进的方法一样令人满意。结果表明，该方法可以在较短的处理时间内准确地从眼底图像中检测到 EX。
