The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
An Adaptive Algorithm for Detection of Exudates Based on Localized Properties of Fundus Images
|
|
Author(s): Katha Chanda (College of Computing, Georgia Institute of Technology, Atlanta, USA), Ashish Issac (Department of Electronics & Communication Engineering, Amity University, Noida, India)and Malay Kishore Dutta (Center for Advanced Studies, Dr. A.P.J Abdul Kalam Technical University, Lucknow, India)
Copyright: 2023
Pages: 21
Source title:
Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7544-7.ch010
Purchase
|
Abstract
This article presents an algorithm to detect exudates, which can be considered as one of the many abnormalities, to identify diabetic retinopathy from fundus images. The algorithm is invariant to illumination and works well on poor contrast images with high reflection noise. The artefacts are correctly rejected despite their colour, intensity and contrast being almost similar to that of exudates. Optic disc is localized and segmented using average filter of specially determined size which is an important step in the rejection of false positives. Exudates are located by generating candidate regions using variance and median filters followed by morphological reconstruction. The strategic selection of local properties to decide the threshold, makes this approach novel and adaptive, that is highly accurate for detection of exudates. The proposed method was tested on two publicly available labelled databases (DIARETDB1 and MESSIDOR) and a database from a local hospital and achieved a sensitivity of 96.765% and a positive predictive value of 93.514%.
Related Content
|
Aylin Gökhan, Kubilay Dogan Kilic, Türker Çavuşoğlu, Yiğit Uyanıkgil.
© 2024.
12 pages.
|
|
Pratyush Panda, Subhalaxmi Das.
© 2024.
21 pages.
|
|
Vikram Singh, Sangeeta Rani.
© 2024.
17 pages.
|
|
Pancham Singh, Mrignainy Kansal, Shirshendu Lahiri, Harshit Vishnoi, Lakshay Mittal.
© 2024.
19 pages.
|
|
Shreeharsha Dash, Subhalaxmi Das.
© 2024.
16 pages.
|
|
V. Sathya, Shalini Parthiban, M. Megavarshini, V. Shenbagaraman, R. Ramya.
© 2024.
13 pages.
|
|
Olalekan Joel Awujoola, Theophilus Enem Aniemeka, Oluwasegun Abiodun Abioye, Abidemi Elizabeth Awujoola, Fiyinfoluwa Ajakaiye, Olayinka Racheal Adelegan.
© 2024.
34 pages.
|
|
|