Literature DB >> 26779642

A Novel Microaneurysms Detection Method Based on Local Applying of Markov Random Field.

Razieh Ganjee1, Reza Azmi2, Mohsen Ebrahimi Moghadam3.   

Abstract

Diabetic Retinopathy (DR) is one of the most common complications of long-term diabetes. It is a progressive disease and by damaging retina, it finally results in blindness of patients. Since Microaneurysms (MAs) appear as a first sign of DR in retina, early detection of this lesion is an essential step in automatic detection of DR. In this paper, a new MAs detection method is presented. The proposed approach consists of two main steps. In the first step, the MA candidates are detected based on local applying of Markov random field model (MRF). In the second step, these candidate regions are categorized to identify the correct MAs using 23 features based on shape, intensity and Gaussian distribution of MAs intensity. The proposed method is evaluated on DIARETDB1 which is a standard and publicly available database in this field. Evaluation of the proposed method on this database resulted in the average sensitivity of 0.82 for a confidence level of 75 as a ground truth. The results show that our method is able to detect the low contrast MAs with the background while its performance is still comparable to other state of the art approaches.

Entities:  

Keywords:  Diabetic retinopathy; Fundus images; Markov random field model; Microaneurysms

Mesh:

Year:  2016        PMID: 26779642     DOI: 10.1007/s10916-016-0434-4

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  22 in total

1.  An improved medical decision support system to identify the diabetic retinopathy using fundus images.

Authors:  S Jerald Jeba Kumar; M Madheswaran
Journal:  J Med Syst       Date:  2012-03-06       Impact factor: 4.460

2.  Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms.

Authors:  Sahar Yousefi; Reza Azmi; Morteza Zahedi
Journal:  Med Image Anal       Date:  2012-02-01       Impact factor: 8.545

3.  A successive clutter-rejection-based approach for early detection of diabetic retinopathy.

Authors:  Keerthi Ram; Gopal Datt Joshi; Jayanthi Sivaswamy
Journal:  IEEE Trans Biomed Eng       Date:  2010-12-03       Impact factor: 4.538

Review 4.  Retinal imaging and image analysis.

Authors:  Michael D Abràmoff; Mona K Garvin; Milan Sonka
Journal:  IEEE Rev Biomed Eng       Date:  2010

5.  Automatic detection of red lesions in digital color fundus photographs.

Authors:  Meindert Niemeijer; Bram van Ginneken; Joes Staal; Maria S A Suttorp-Schulten; Michael D Abràmoff
Journal:  IEEE Trans Med Imaging       Date:  2005-05       Impact factor: 10.048

6.  Automatic detection of microaneurysms in color fundus images.

Authors:  Thomas Walter; Pascale Massin; Ali Erginay; Richard Ordonez; Clotilde Jeulin; Jean-Claude Klein
Journal:  Med Image Anal       Date:  2007-05-26       Impact factor: 8.545

7.  Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy.

Authors:  B Dupas; T Walter; A Erginay; R Ordonez; N Deb-Joardar; P Gain; J-C Klein; P Massin
Journal:  Diabetes Metab       Date:  2010-03-10       Impact factor: 6.041

8.  Automated detection of microaneurysms in digital red-free photographs: a diabetic retinopathy screening tool.

Authors:  J H Hipwell; F Strachan; J A Olson; K C McHardy; P F Sharp; J V Forrester
Journal:  Diabet Med       Date:  2000-08       Impact factor: 4.359

9.  Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy.

Authors:  Usman M Akram; Shoab A Khan
Journal:  J Med Syst       Date:  2011-11-17       Impact factor: 4.460

10.  Automated identification of diabetic retinopathy stages using digital fundus images.

Authors:  Jagadish Nayak; P Subbanna Bhat; Rajendra Acharya; C M Lim; Manjunath Kagathi
Journal:  J Med Syst       Date:  2008-04       Impact factor: 4.460

View more
  1 in total

1.  A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

Authors:  Somasundaram S K; Alli P
Journal:  J Med Syst       Date:  2017-11-09       Impact factor: 4.460

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.