The new approach to automatic detection of Optic Disc from non-dilated retinal images

Vahabi, Zahra; Vafadoost, M.; Gharibzadeh, Shahriar

doi:10.1109/icbme.2010.5704960

Cited by 3 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 9 ], the authors utilized basic operations like edge detection, binary thresholding, and morphological operation for detecting the optic disc and blood vessel pixels. In [ 10 ], the authors described a new filtering approach which includes Sobel edge detection, texture analysis, intensity, and template matching methods for the detection of the optic disc. In [ 11 ], the optic disc boundary was approximated by means of edge detection, morphological operations, and circular Hough transformation.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

Akyol

Şen

Bayir

2016

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

show abstract

Section: Literature Reviewmentioning

confidence: 99%