Open AccessEfficient approach for the automatic detection of haemorrhages in colour retinal images
Author(s) -
Bhoopalan Ramasubramanian,
Sundaramoorthy Selvaperumal
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.1036
Subject(s) - computer science , artificial intelligence , retinal , diabetic retinopathy , computer vision , robustness (evolution) , receiver operating characteristic , classifier (uml) , cotton wool spots , pattern recognition (psychology) , medicine , ophthalmology , machine learning , diabetes mellitus , biochemistry , chemistry , gene , endocrinology
Advances in the eye care telemedicine system aid the diabetic patients in remote areas to stop the unwanted visit to ophthalmologist, reduces overall cost, time and money. Diabetic retinopathy, which is the primary cause of sight loss, has the most common symptoms like microaneurysms, haemorrhages, cotton‐wool spots, exudates and drusen. In this work, an efficient approach for the automatic detection of haemorrhages in colour retinal images is proposed and validated. The colour retinal images captured from the diabetic patients are enhanced by an effective pre‐processor. A bag of features based on intensity, colour and texture are extracted. Finally, the features are classified with the help of partial least square classifier. The classifier performance is validated on two publicly available databanks. The developed method obtains an area under receiver operating characteristic curve of 0.98 with an average execution time of 6 s. This application outperforms the existing approaches with high robustness and efficiency.