Annotating Retina Fundus Images for Teaching and Learning Diabetic Retinopathy Software

Ralibul Alam, Bruce Poon, Mahmood Kazi Mohammed, M. Ashraful Amin, Hong Yan

Abstract


With the improvement in IT industry, more and more application of computer software is introduced in teaching and learning. In this paper, we discuss the development process of such software. Diabetic Retinopathy is a common complication for diabetic patients. It may cause sight loss if not treated early. There are several stages of this disease. Fundus imagery is required to identify the stage and severity of the disease. Due to the lack of proper dataset of the fundus images and proper annotation, it is very difficult to perform research on this topic. Moreover, medical students are often facing difficulty with identifying the diseases in later stage of their practice as they may not have seen a sample of all of the stages of Diabetic Retinopathy problems. To mitigate the problem, we have collected fundus images from different geographic area of Bangladesh and designed an annotation software to store information about the patient, the infection level and their locations in the images. Sometimes, it is difficult to select all appropriate pixels of the infected region. To resolve the issue, we have introduced a K nearest neighbor (KNN) based technique to accurately select the region of interest (ROI). Once an expert (ophthalmologist) has annotated the images, the software can be used by the students for learning.

Keywords


Teaching and Learning Software; Diabetic Retinopathy; Medical Image Annotation; Expert Medical System; K-Nearest Neighbor;

References


J. J. Kanski and B. Bowling, Clinical Opthalmologhy, 7th ed., Elsevier, 2011, pp. 535-554.

M. Ashraful Amin and Hong Yan, “High Speed Detectionof Blood Vessels in a Retinal Fundus Image using Phase Congruency,” Soft Computing, 2011, Springer.

M. Islamuddin Ahmed, M. Ashraful Amin, “High Speed Detection of Optical Disc in Retinal Fundus Image”, Journal of Signal, Image and Video Processing, 2012, Springer.

M. Islamuddin Ahmed, M. Ashraful Amin, Bruce Poon, and Hong Yan, “Retina Based Biometric Authentication using Phase Congruency”, IJMLC, 2013, Springer.

Digital Retinal Image for Vessel Extraction (DRIVE). Available at: http://www.isi.uu.nl/Research/Databases/DRIVE/

VARPA Retina Images Authentication (VARIA) database. Available at: http://www.varpa.es/varia.html

VICAVR Database, http://www.varpa.es/vicavr.html

DIARETDB_01: Standard Diabetic Retinopathy Database. Available at: http://www2.it.lut.fi/project/imageret/diaretdb1/index.html

MESSIDOR: Digital Retinal Images. Available at: http://messidor.crihan.fr/download.php

T. Cover, and P. Hart. "Nearest neighbor pattern classification," IEEE Transl. Information Theory, vol. 13, issue. 1, pp. 21-27, 1967.


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