Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction

Abstract

We introduce here a new technique for segmenting optic cup using two-dimensional fundus images. Cup segmentation is the most challenging part of image processing of the optic nerve head due to the complexity of its structure. Using the blood vessels to segment the cup is important. Here, we report on blood vessel extraction using first a top-hat transform and Otsu’s segmentation function to detect the curves in the blood vessels (kinks) which indicate the cup boundary. This was followed by an interval type-II fuzzy entropy procedure. Finally, the Hough transform was applied to approximate the cup boundary. The algorithm was evaluated on 550 fundus images from a large dataset, which contained three different sets of images, where the cup was manually marked by six ophthalmologists. On one side, the accuracy of the algorithm was tested on the three image sets independently. The final cup detection accuracy in terms of area and centroid was calculated to be 78.2% of 441 images. Finally, we compared the algorithm performance with manual markings done by the six ophthalmologists. The agreement was determined between the ophthalmologists as well as the algorithm. The best agreement was between ophthalmologists one, two and five in 398 of 550 images, while the algorithm agreed with them in 356 images.

Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

“…Many different approaches to segmenting of the OD and/or OC in fundus images have been proposed in the literature. The existing methods for automated OD and OC segmentation in fundus images can be broadly classified into three main categories: shape-based template matching [3][4][5][6][7][8][9], active contours and deformable based models [10][11][12][13][14][15][16][17][18], and more recently, machine and deep learning methods [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. We give a brief overview of the existing methods below.…”

“…We give a brief overview of the existing methods below. (a) Shape-based and template matching models: These methods model the OD as a circular or elliptical object and try to fit a circle using the Hough transform [4,5,8,9], an ellipse [3,6] or a rounded curve using a sliding band filter [7]. These approaches typically feature in the earlier work in optic disc and cup segmentation.…”

An Automatic Image Processing System for Glaucoma Screening

“…On the other hand, the cup segmentation was introduced by Almazroa et al [ 12 ]. The blood vessels were extracted using the same approach as that used for optic disc segmentation.…”

“…This paper gives the results from calculations of the horizontal and vertical cup to disc ratios using our previously introduced optic disc [ 11 ] and cup [ 12 ] algorithms. The algorithms were tested using the RIGA dataset.…”

<p>The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System</p>

“…The automated optic nerve head segmentation system [10][11][12][13][14] Glaucoma Analysis (RIGA) dataset. 15 In brief, this automated system was developed and tested based on six experienced ophthalmologists, who established manual markings of the disc and cup boundaries drawn on digital optic disc photos on a computer tablet as shown in Figure 2.…”