Literature DB >> 20952329

Automatic optic disc detection from retinal images by a line operator.

Shijian Lu1, Joo Hwee Lim.   

Abstract

Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. A line operator is designed to capture such circular brightness structure, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and 81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained.

Entities:  

Mesh:

Year:  2010        PMID: 20952329     DOI: 10.1109/TBME.2010.2086455

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  8 in total

1.  Automated identification of exudates and optic disc based on inverse surface thresholding.

Authors:  Haniza Yazid; Hamzah Arof; Hazlita Mohd Isa
Journal:  J Med Syst       Date:  2011-02-12       Impact factor: 4.460

2.  A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model.

Authors:  Ahmad S Abdullah; Javad Rahebi; Yasa Ekşioğlu Özok; Mohanad Aljanabi
Journal:  Med Biol Eng Comput       Date:  2019-08-24       Impact factor: 2.602

3.  PCA-based localization approach for segmentation of optic disc.

Authors:  Varun P Gopi; M S Anjali; S Issac Niwas
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-09-30       Impact factor: 2.924

4.  Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy.

Authors:  Mohamed A Berbar
Journal:  Health Inf Sci Syst       Date:  2022-06-29

Review 5.  Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends.

Authors:  Baoxin Li; Helen K Li
Journal:  Curr Diab Rep       Date:  2013-08       Impact factor: 4.810

6.  Detection and segmentation of erythrocytes in blood smear images using a line operator and watershed algorithm.

Authors:  Hassan Khajehpour; Alireza Mehri Dehnavi; Hossein Taghizad; Esmat Khajehpour; Mohammadreza Naeemabadi
Journal:  J Med Signals Sens       Date:  2013-07

Review 7.  A Review on Recent Developments for Detection of Diabetic Retinopathy.

Authors:  Javeria Amin; Muhammad Sharif; Mussarat Yasmin
Journal:  Scientifica (Cairo)       Date:  2016-09-29

Review 8.  A review on automatic analysis techniques for color fundus photographs.

Authors:  Renátó Besenczi; János Tóth; András Hajdu
Journal:  Comput Struct Biotechnol J       Date:  2016-10-06       Impact factor: 7.271

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.