Region-based multi-step optic disk and cup segmentation from color fundus image

One of the greatest concerns to the personnel in the current health care sector is the severe progression of diabetes. People can often have diabetes and be completely unaware as the symptoms seem harmless when they are seen on their own. Diabetic retinopathy (DR) is an eye disease that is associated with long-standing diabetes. Retinopathy can occur with all types of diabetes and can lead to blindness if left untreated. The conventional method followed by ophthalmologists is the regular testing of the retina. As this method takes time and energy of the ophthalmologists, a new feature-based automated technique for classification and detection of exudates in color fundus image is proposed in this paper. This method reduces the work of the professionals while examining every fundus image rather than only on abnormal image. The exudates are detected from the color fundus image by applying a few pre-processing techniques that remove the optic disk and similar blood vessels using morphological operations. The pre-processed image was then applied for feature extraction and these features were utilized for classification purpose. In this paper, a novel classification technique such as self-adaptive resource allocation network (SRAN) and meta-cognitive neural network (McNN) classifier is employed for classification of images as exudates, their severity and nonexudates. SRAN classifier makes use of self-adaptive thresholds to choose the appropriate training samples and removes the redundant samples to prevent over-training. These selected samples are availed to improve the classification performance. McNN classifier employs human-like meta-cognition to regulate the sequential learning process. The meta-cognitive component controls the learning process in the cognitive component by deciding what-to-learn, when-to-learn and how-to-learn. It is therefore evident that the implementation of human meta-cognitive learning principle improves efficient learning.

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New Hypertensive Retinopathy Grading Based on the Ratio of Artery Venous Diameter from Retinal Image

International Journal of Computing ◽

10.47839/ijc.20.2.2169 ◽

2021 ◽

pp. 221-227

Author(s):

Bambang Krismono Triwijoyo ◽

Boy Subirosa Sabarguna ◽

Widodo Budiharto ◽

Edi Abdurachman

Keyword(s):

Region Of Interest ◽

Vessel Diameter ◽

Optic Disk ◽

Hypertensive Retinopathy ◽

Fundus Image ◽

Image Resizing ◽

The Public ◽

Retinal Blood Vessels ◽

Color Fundus Image ◽

Venous Diameter

Medical research indicated that narrowing of the retinal blood vessels might be an early indicator of cardiovascular diseases; one of them is hypertensive retinopathy. This paper proposed the new staging method of hypertensive retinopathy by measure the ratio of diameter artery and vein (AVR). The dataset used in this research is the public Messidor color fundus image dataset. The proposed method consists of image resizing using bicubic interpolation, optic disk detection, a region of interest computation, vessel diameter measuring, AVR calculation, and grading the new categories of Hypertensive Retinopathy based on Keith-Wagener-Barker categories. The experiments show that the proposed method can determine the stage of hypertensive retinopathy into new categories.

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