Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review

Superpixel Based Retinal Area Detection in SLO Images

“…There are several image based features which have been represent different retinal structures in fundus images such as colour, illumination, intensity, skewness, texture, histogram, sharpness etc [4,14,5]. For reducing computational complexity, grid analysis containing small patches of the image has been proposed.…”

A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis

“…training one part whilst testing the other. (4,8), N R (16), N xxR (16), (8), 16,4,G , L u,uG (8), (16) , N G (16), I mn (4, 7), BY (4, 7), I mn (4,7), I mn (3,7), (2), (2), L uvG (4), (8) The Drishti dataset has been tested on the model built upon the RIMONE dataset.…”

“…Therefore, accurate automatic boundary detection of optic disc and cup is critical for the diagnosis of glaucoma. Some research efforts have been made on automatic segmentation of the optic disc or optic cup [4][5][6][7][8][9][10][11]. These efforts can be broadly divided into two categories: non-modelbased and model-based approaches.…”

Retinal Area Detector From Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases

“…These diagnostic techniques are time consuming. Automated analysis of retinal images has the potential to reduce the time which clinicians need to look at the images which can expect more patients to be screened and more consistent diagnoses can be given in a time efficient manner [12].…”

“…The characterisation of retinal images were performed in terms of image features such as intensity, skewness, textural analysis, histogram analysis, sharpness etc [8], [12], [28]. Dias et al [9] determined four different classifiers using four types of features.…”