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An Incremental Feature Extraction Framework for Referable Diabetic Retinopathy Detection

机译：用于参考性糖尿病性视网膜病变检测的增量特征提取框架

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摘要

Diabetic retinopathy (DR) might be characterized by the occurrence of lesions in the retinal image. Existing approaches require a large set of retinal images where lesions in the image are individually annotated to learn a model that will classify an image as referable or non-referable DR. However, annotating individual lesions is a tedious task and the accuracy of the learnt model is limited by the availability of these annotated images. In this paper, we first learn a universal Gaussian mixture model (GMM) from a small set of annotated images. This universal GMM is then applied as the prior belief to learn an adaptive GMM for individual images. The proposed approach aims to capture the characteristics of referable versus non-referable images by examining the difference between the universal GMM and the adaptive GMM. An image-level classifier is then built based on these differences as features. Experimental results on three fundus image datasets (MESSIDOR, DIARETDB1 and SORC) indicate that the proposed framework achieves 92.1%, 97.68% and 87.1% ROC area values respectively. This approach also opens up a way to use the widely available public fundus images, where the images are labelled but not annotated, for progressively refining the universal GMM leading to an improved performance of approximately 5% and 1% respectively for SORC and MESSIDOR dataset after five refinement steps.
机译：糖尿病性视网膜病（DR）可能以视网膜图像中出现病变为特征。现有方法需要大量的视网膜图像，其中分别注释图像中的病变以学习将图像分类为可参考或不可参考DR的模型。但是，对单个病变进行批注是一项繁琐的工作，学习模型的准确性受到这些批注图像的可用性的限制。在本文中，我们首先从一小组带注释的图像中学习通用高斯混合模型（GMM）。然后，将此通用GMM用作先验信念，以学习单个图像的自适应GMM。所提出的方法旨在通过检查通用GMM和自适应GMM之间的差异来捕获可参考图像与不可参考图像的特征。然后基于这些差异作为特征构建图像级分类器。在三个眼底图像数据集（MESSIDOR，DIARETDB1和SORC）上的实验结果表明，提出的框架分别实现了92.1％，97.68％和87.1％的ROC面积值。这种方法还开辟了一种使用广泛可用的公共眼底图像的方法，在这些图像上标记了图像但未进行注释，以逐步完善通用GMM，从而在经过SRC和MESSIDOR数据集处理后，分别将性能提高了约5％和1％。五个优化步骤。

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