Detección automática de microaneurismas en retinografías

Jiménez, Soledad Ballesteros; Alemany, P.; Núñez-Benjumea, Francisco J.; Serrano, Carmen; Acha, Begoña; Fondón, Irene; Carral, Florentino; Sánchez, Clara I.

doi:10.1016/j.oftal.2011.04.015

Cited by 4 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore the input image to our system has green channel extracted only. An iterative algorithm proposed in is used to remove the strong contrast between the retinal fundus and image outside the aperture [ 5 ]. The Gabor wavelet transform is implemented using the fast fourier transformation algorithm.…”

Section: Methodsmentioning

confidence: 99%

A computational modeling for the detection of diabetic retinopathy severity

et al. 2014

View full text Add to dashboard Cite

Prolonged diabetes ultimately leads to Diabetic Retinopathy (DR) which is one of the leading causes of preventable blindness in the world. Through advanced image analysis techniques are used for abnormalities detection in retina that define and correlate the severity of DR. A thorough study is done in this area in recent past years and on the basis of these studies we have developed a computer based prediction model that is used to determine the severity of DR. To identify severity DR, we have analyzed the human eye image. We have extracted some important features from human eye image i.e. Blood Artery, Optical disc, Exudates. Based on these image and data we have designed an automated system for the determination of DR severity. This automated DR severity assessment methods can be used to predict the clinical case and conditions when young clinicians would agree or disagree with their more experienced fellow members. The algorithms described in this study may be used in clinical practice to validate or invalidate the diagnoses. Algorithms or method developed here may also be used for pooling diagnostic knowledge for serving mankind. Here we have described a computational based low cost retinal diagnostic approach which can aid an ophthalmologist to quickly diagnose the various stages of DR. This system can accept retinal images and can successfully detect any pathological condition associated with DR.

show abstract

Section: Methodsmentioning

confidence: 99%

A computational modeling for the detection of diabetic retinopathy severity

et al. 2014

View full text Add to dashboard Cite

show abstract

Automated detection of microaneurysm for fundus images

Mohammad

Omar

Supriyanto

et al. 2016

2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)

View full text Add to dashboard Cite

Mathematical morphology for microaneurysm detection in fundus images

Joshi

Karule

2019

European Journal of Ophthalmology

View full text Add to dashboard Cite

Aim: Fundus image analysis is the basis for the better understanding of retinal diseases which are found due to diabetes. Detection of earlier markers such as microaneurysms that appear in fundus images combined with treatment proves beneficial to prevent further complications of diabetic retinopathy with an increased risk of sight loss. Methods: The proposed algorithm consists of three modules: (1) image enhancement through morphological processing; (2) the extraction and removal of red structures, such as blood vessels preceded by detection and removal of bright artefacts; (3) finally, the true microaneurysm candidate selection among other structures based on feature extraction set. Results: The proposed strategy is successfully evaluated on two publicly available databases containing both normal and pathological images. The sensitivity of 89.22%, specificity of 91% and accuracy of 92% achieved for the detection of microaneurysms for Diaretdb1 database images. The algorithm evaluation for microaneurysm detection has a sensitivity of 83% and specificity 82% for e-ophtha database. Conclusion: In automated detection system, the successful detection of the number of microaneurysms correlates with the stages of the retinal diseases and its early diagnosis. The results for true microaneurysm detection indicates it as a useful tool for screening colour fundus images, which proves time saving for counting of microaneurysms to follow Diabetic Retinopathy Grading Criteria.

show abstract

Detección automática de microaneurismas en retinografías

Cited by 4 publications

References 14 publications

A computational modeling for the detection of diabetic retinopathy severity

A computational modeling for the detection of diabetic retinopathy severity

Automated detection of microaneurysm for fundus images

Mathematical morphology for microaneurysm detection in fundus images

Contact Info

Product

Resources

About