Automated detection of diabetic retinopathy through image feature extraction

M. Akshatha Rao; Dharmanna Lamani; Rekha Bhandarkar; T. C. Manjunath

doi:10.1109/ICAECC.2014.7002402

Automated detection of diabetic retinopathy through image feature extraction

M. Akshatha Rao
, Dharmanna Lamani
, Rekha Bhandarkar
, T. C. Manjunath

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

22 Citations (Scopus)

Abstract

Diabetes is a disease which is caused due to high blood glucose level in the body. If diabetes is left untreated, vision of the diabetic patient will deteriorate as the disease progresses. Vision deteriorates due to the development of various lesions in eye retina such as microaneurysms, exudates, hemorrhages and cotton wool spots; diabetes at this stage is called Diabetic Retinopathy (DR). Vision remains stable during early stages but as the disease progress and if left untreated it leads to blindness. In this paper, an automated diagnosis of DR using a new approach called Hurst Exponent to determine Fractal Dimension (FD) is presented. Various features like Contrast, Correlation, Energy, Homogeneity, and Entropy are extracted from gray level co-occurrence matrix of image. The statistical analysis of DR and Healthy Retinopathy for various extracted features is presented. The Power Spectrum is obtained for input retinal image, which helps ophthalmologist to quickly diagnose DR on visual basis.

Original language	English
Title of host publication	2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479954964
DOIs	https://doi.org/10.1109/ICAECC.2014.7002402
Publication status	Published - 06-01-2015
Event	2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014 - Bangalore, India Duration: 10-10-2014 → 11-10-2014

Publication series

Name	2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014

Conference

Conference	2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014
Country/Territory	India
City	Bangalore
Period	10-10-14 → 11-10-14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Electrical and Electronic Engineering
Computational Theory and Mathematics

Access to Document

10.1109/ICAECC.2014.7002402

Cite this

Rao, M. A., Lamani, D., Bhandarkar, R., & Manjunath, T. C. (2015). Automated detection of diabetic retinopathy through image feature extraction. In 2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014 Article 7002402 (2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAECC.2014.7002402

Rao, M. Akshatha ; Lamani, Dharmanna ; Bhandarkar, Rekha et al. / Automated detection of diabetic retinopathy through image feature extraction. 2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014. Institute of Electrical and Electronics Engineers Inc., 2015. (2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014).

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title = "Automated detection of diabetic retinopathy through image feature extraction",

abstract = "Diabetes is a disease which is caused due to high blood glucose level in the body. If diabetes is left untreated, vision of the diabetic patient will deteriorate as the disease progresses. Vision deteriorates due to the development of various lesions in eye retina such as microaneurysms, exudates, hemorrhages and cotton wool spots; diabetes at this stage is called Diabetic Retinopathy (DR). Vision remains stable during early stages but as the disease progress and if left untreated it leads to blindness. In this paper, an automated diagnosis of DR using a new approach called Hurst Exponent to determine Fractal Dimension (FD) is presented. Various features like Contrast, Correlation, Energy, Homogeneity, and Entropy are extracted from gray level co-occurrence matrix of image. The statistical analysis of DR and Healthy Retinopathy for various extracted features is presented. The Power Spectrum is obtained for input retinal image, which helps ophthalmologist to quickly diagnose DR on visual basis.",

author = "Rao, \{M. Akshatha\} and Dharmanna Lamani and Rekha Bhandarkar and Manjunath, \{T. C.\}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014 ; Conference date: 10-10-2014 Through 11-10-2014",

year = "2015",

month = jan,

day = "6",

doi = "10.1109/ICAECC.2014.7002402",

language = "English",

series = "2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014",

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Rao, MA, Lamani, D, Bhandarkar, R & Manjunath, TC 2015, Automated detection of diabetic retinopathy through image feature extraction. in 2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014., 7002402, 2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014, Institute of Electrical and Electronics Engineers Inc., 2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014, Bangalore, India, 10-10-14. https://doi.org/10.1109/ICAECC.2014.7002402

Automated detection of diabetic retinopathy through image feature extraction. / Rao, M. Akshatha; Lamani, Dharmanna; Bhandarkar, Rekha et al.
2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014. Institute of Electrical and Electronics Engineers Inc., 2015. 7002402 (2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Automated detection of diabetic retinopathy through image feature extraction

AU - Rao, M. Akshatha

AU - Lamani, Dharmanna

AU - Bhandarkar, Rekha

AU - Manjunath, T. C.

PY - 2015/1/6

Y1 - 2015/1/6

N2 - Diabetes is a disease which is caused due to high blood glucose level in the body. If diabetes is left untreated, vision of the diabetic patient will deteriorate as the disease progresses. Vision deteriorates due to the development of various lesions in eye retina such as microaneurysms, exudates, hemorrhages and cotton wool spots; diabetes at this stage is called Diabetic Retinopathy (DR). Vision remains stable during early stages but as the disease progress and if left untreated it leads to blindness. In this paper, an automated diagnosis of DR using a new approach called Hurst Exponent to determine Fractal Dimension (FD) is presented. Various features like Contrast, Correlation, Energy, Homogeneity, and Entropy are extracted from gray level co-occurrence matrix of image. The statistical analysis of DR and Healthy Retinopathy for various extracted features is presented. The Power Spectrum is obtained for input retinal image, which helps ophthalmologist to quickly diagnose DR on visual basis.

AB - Diabetes is a disease which is caused due to high blood glucose level in the body. If diabetes is left untreated, vision of the diabetic patient will deteriorate as the disease progresses. Vision deteriorates due to the development of various lesions in eye retina such as microaneurysms, exudates, hemorrhages and cotton wool spots; diabetes at this stage is called Diabetic Retinopathy (DR). Vision remains stable during early stages but as the disease progress and if left untreated it leads to blindness. In this paper, an automated diagnosis of DR using a new approach called Hurst Exponent to determine Fractal Dimension (FD) is presented. Various features like Contrast, Correlation, Energy, Homogeneity, and Entropy are extracted from gray level co-occurrence matrix of image. The statistical analysis of DR and Healthy Retinopathy for various extracted features is presented. The Power Spectrum is obtained for input retinal image, which helps ophthalmologist to quickly diagnose DR on visual basis.

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DO - 10.1109/ICAECC.2014.7002402

M3 - Conference contribution

AN - SCOPUS:84956739612

T3 - 2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014

BT - 2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014

Y2 - 10 October 2014 through 11 October 2014

ER -

Rao MA, Lamani D, Bhandarkar R, Manjunath TC. Automated detection of diabetic retinopathy through image feature extraction. In 2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014. Institute of Electrical and Electronics Engineers Inc. 2015. 7002402. (2014 International Conference on Advances in Electronics, Computers and Communications, ICAECC 2014). doi: 10.1109/ICAECC.2014.7002402

Automated detection of diabetic retinopathy through image feature extraction

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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