Unsupervised feature extraction of anterior chamber OCT images for ordering and classification

被引：14

作者：

Amil, Pablo ^{[1
]}

Gonzalez, Laura ^{[2
]}

Arrondo, Elena ^{[2
]}

Salinas, Cecilia ^{[2
]}

Guell, J. L. ^{[2
]}

Masoller, Cristina ^{[1
]}

Parlitz, Ulrich ^{[3
]}

机构：

[1] Univ Politecn Cataluna, Rambla St Nebridi 22, Terrassa 08222, Spain

[2] Inst Microcirugia Ocular, Josep Maria Llado 3, Barcelona 08035, Spain

[3] Max Planck Inst Dynam & Self Org, Fassberg 17, D-37077 Gottingen, Germany

来源：

SCIENTIFIC REPORTS | 2019年 / 9卷 / 1期

基金：

欧盟地平线“2020”;

关键词：

OPTICAL COHERENCE TOMOGRAPHY; MACHINE LEARNING CLASSIFIERS; GLAUCOMA; DIAGNOSIS;

D O I：

10.1038/s41598-018-38136-8

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

070301 [无机化学]; 070403 [天体物理学]; 070507 [自然资源与国土空间规划学]; 090105 [作物生产系统与生态工程];

摘要：

We propose an image processing method for ordering anterior chamber optical coherence tomography (OCT) images in a fully unsupervised manner. The method consists of three steps: Firstly we preprocess the images (filtering the noise, aligning and normalizing the resolution); secondly, a distance measure between images is computed for every pair of images; thirdly we apply a machine learning algorithm that exploits the distance measure to order the images in a two-dimensional plane. The method is applied to a large (similar to 1000) database of anterior chamber OCT images of healthy subjects and patients with angle-closure and the resulting unsupervised ordering and classification is validated by two ophthalmologists.

引用

页数：9

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