Morales Sandra, Engan Kjersti, Naranjo Valery, Colomer Adrian
IEEE J Biomed Health Inform. 2017 Jan;21(1):184-192. doi: 10.1109/JBHI.2015.2490798. Epub 2015 Oct 14.
This paper investigates discrimination capabilities in the texture of fundus images to differentiate between pathological and healthy images. For this purpose, the performance of local binary patterns (LBP) as a texture descriptor for retinal images has been explored and compared with other descriptors such as LBP filtering and local phase quantization. The goal is to distinguish between diabetic retinopathy (DR), age-related macular degeneration (AMD), and normal fundus images analyzing the texture of the retina background and avoiding a previous lesion segmentation stage. Five experiments (separating DR from normal, AMD from normal, pathological from normal, DR from AMD, and the three different classes) were designed and validated with the proposed procedure obtaining promising results. For each experiment, several classifiers were tested. An average sensitivity and specificity higher than 0.86 in all the cases and almost of 1 and 0.99, respectively, for AMD detection were achieved. These results suggest that the method presented in this paper is a robust algorithm for describing retina texture and can be useful in a diagnosis aid system for retinal disease screening.
本文研究了眼底图像纹理中的辨别能力，以区分病理图像和健康图像。为此，探索了局部二值模式（LBP）作为视网膜图像纹理描述符的性能，并与其他描述符（如LBP滤波和局部相位量化）进行了比较。目标是通过分析视网膜背景纹理来区分糖尿病视网膜病变（DR）、年龄相关性黄斑变性（AMD）和正常眼底图像，同时避免先前的病变分割阶段。设计了五个实验（将DR与正常图像分离、将AMD与正常图像分离、将病理图像与正常图像分离、将DR与AMD分离以及区分三种不同类别），并使用所提出的程序进行了验证，取得了有前景的结果。对于每个实验，测试了多个分类器。在所有情况下，平均灵敏度和特异性均高于0.86，对于AMD检测，灵敏度和特异性分别几乎达到1和0.99。这些结果表明，本文提出的方法是一种用于描述视网膜纹理的稳健算法，可用于视网膜疾病筛查的诊断辅助系统。
