当前位置: X-MOL 学术IET Image Process. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Classification of dry age-related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning
IET Image Processing ( IF 2.3 ) Pub Date : 2020-06-01 , DOI: 10.1049/iet-ipr.2018.6186
Elahe Mousavi 1 , Rahele Kafieh 2 , Hossein Rabbani 2
Affiliation  

Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the major causes of vision loss in developed countries. Alteration of retinal layer structure and appearance of exudates are the most significant signs of these diseases. In this paper, with the aim of automatic classification of DME, AMD, and normal subjects using Optical Coherence Tomography (OCT) images, a dictionary-learning based classification is proposed. The two important issues intended in this approach are avoiding retinal layer segmentation and attempting to mimic the authors' understanding based on normal and abnormal region identifications, considering that the signs of diseases appear in a small fraction of B-Scans. The histogram of oriented gradients feature descriptor was utilized to characterize the distribution of local intensity gradients and edge directions. To capture the structure of extracted features, different dictionary learning-based classifiers are employed. The dataset consists of 45 subjects: 15 patients with AMD, 15 patients with DME, and 15 normal subjects. The proposed classifier leads to an accuracy of 95.13, 100.00, and 100.00% for DME, AMD, and normal OCT images, respectively, only by considering 4% of all B-Scans of a volume, which outperforms the state-of-the-art methods.

中文翻译:

利用字典学习从光学相干断层扫描图像中分类与干龄相关的黄斑变性和糖尿病性黄斑水肿

与年龄有关的黄斑变性(AMD)和糖尿病性黄斑水肿(DME)是发达国家视力丧失的主要原因。视网膜层结构的改变和渗出液的出现是这些疾病的最明显迹象。本文针对使用光学相干断层扫描(OCT)图像对DME,AMD和正常对象进行自动分类的目的,提出了一种基于字典学习的分类方法。考虑到疾病的征兆出现在B扫描的一小部分中,此方法旨在解决的两个重要问题是避免视网膜层分割,并尝试根据正常和异常区域识别来模仿作者的理解。定向梯度特征描述符的直方图用于表征局部强度梯度和边缘方向的分布。为了捕获提取特征的结构,采用了不同的基于字典学习的分类器。该数据集由45位受试者组成:15位AMD患者,15位DME患者和15位正常受试者。提议的分类器仅通过考虑体积的所有B扫描的4%即可分别对DME,AMD和正常OCT图像产生95.13、100.00和100.00%的准确度,其性能优于艺术方法。
更新日期:2020-06-01
down
wechat
bug