title: Retinal status analysis method based on feature extraction and quantitative grading in OCT images.
abstract: BACKGROUND
  Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis.
  
  
  METHODS
  This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images.
  
  
  RESULTS
  This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status.
  
  
  CONCLUSIONS
  This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis.
authors.affiliation: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing, China. fdm2003@163.com.
authors.forename: Dongmei
authors.identifier: http://orcid.org/0000-0002-9800-8603
authors.initials: D
authors.lastname: Fu
authors.affiliation: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing, China.
authors.forename: Hejun
authors.initials: H
authors.lastname: Tong
authors.affiliation: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing, China.
authors.forename: Shuang
authors.initials: S
authors.lastname: Zheng
authors.affiliation: The 306th Hospital of People's Liberation Army, Beijing, China.
authors.forename: Ling
authors.initials: L
authors.lastname: Luo
authors.affiliation: The 306th Hospital of People's Liberation Army, Beijing, China.
authors.forename: Fulin
authors.initials: F
authors.lastname: Gao
authors.affiliation: Dept. of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Czech, Brno, Czech Republic.
authors.forename: Jiri
authors.initials: J
authors.lastname: Minar
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D003657:Decision Making; D003936:Diagnosis, Computer-Assisted; D005654:Fundus Oculi; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008875:Middle Aged; D012160:Retina / Q000000981:diagnostic imaging*; D059629:Signal-To-Noise Ratio; D041623:Tomography, Optical Coherence; D055815:Young Adult
keywords: Feature quantification; Grade evaluation; Image processing; Morphological characterization; Retinal OCT images