Automatic quality assessment and segmentation of diabetic retinopathy images

Worapan Kusakunniran; Jirat Rattanachoosin; Krittanat Sutassananon; Phuthimeth Anekkitphanich

doi:10.1109/TENCON.2016.7848155

Automatic quality assessment and segmentation of diabetic retinopathy images

Worapan Kusakunniran
, Jirat Rattanachoosin
, Krittanat Sutassananon
, Phuthimeth Anekkitphanich

Computer Science Academic Group

Mahidol University

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

4 Citations (Scopus)

Abstract

Diabetes is considered to be one of the most dangerous genetic disorders. It could cause the loss of sight in the case of the diabetic retinopathy. Several medical technologies have been developed and improved to cure the diabetes and overcome the lacks of human experts. This can be done by replacing the manual process using human labors with the automatic diagnosis. In order to achieve this purpose, it requires two main steps which will be focused in this paper. They are the quality assessment and the segmentation of diabetic retinopathy images. In the image quality assessment, four features (namely color, contrast, focus, and illumination) have been investigated. As a result, the contrast histogram in the Principal Component Analysis (PCA) space is used. In the image segmentation, the histogram equalization is used in the pre-processing. Then, the image segmentation based on the iterative selection and the grabcut algorithm is applied. The experimental results demonstrate that the proposed method can achieve very promising performance.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	997-1000
Number of pages	4
ISBN (Electronic)	9781509025961
DOIs	https://doi.org/10.1109/TENCON.2016.7848155
Publication status	Published - 8 Feb 2017
Event	2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore Duration: 22 Nov 2016 → 25 Nov 2016

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2016 IEEE Region 10 Conference, TENCON 2016
Country/Territory	Singapore
City	Singapore
Period	22/11/16 → 25/11/16

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

automated medical diagnosis
diabetic retinopathy
fundus image
image quality assessment
image segmentation

Access to Document

10.1109/TENCON.2016.7848155

Cite this

Kusakunniran, W., Rattanachoosin, J., Sutassananon, K., & Anekkitphanich, P. (2017). Automatic quality assessment and segmentation of diabetic retinopathy images. In Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016 (pp. 997-1000). Article 7848155 (IEEE Region 10 Annual International Conference, Proceedings/TENCON). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2016.7848155

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title = "Automatic quality assessment and segmentation of diabetic retinopathy images",

abstract = "Diabetes is considered to be one of the most dangerous genetic disorders. It could cause the loss of sight in the case of the diabetic retinopathy. Several medical technologies have been developed and improved to cure the diabetes and overcome the lacks of human experts. This can be done by replacing the manual process using human labors with the automatic diagnosis. In order to achieve this purpose, it requires two main steps which will be focused in this paper. They are the quality assessment and the segmentation of diabetic retinopathy images. In the image quality assessment, four features (namely color, contrast, focus, and illumination) have been investigated. As a result, the contrast histogram in the Principal Component Analysis (PCA) space is used. In the image segmentation, the histogram equalization is used in the pre-processing. Then, the image segmentation based on the iterative selection and the grabcut algorithm is applied. The experimental results demonstrate that the proposed method can achieve very promising performance.",

keywords = "automated medical diagnosis, diabetic retinopathy, fundus image, image quality assessment, image segmentation",

author = "Worapan Kusakunniran and Jirat Rattanachoosin and Krittanat Sutassananon and Phuthimeth Anekkitphanich",

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Kusakunniran, W, Rattanachoosin, J, Sutassananon, K & Anekkitphanich, P 2017, Automatic quality assessment and segmentation of diabetic retinopathy images. in Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016., 7848155, IEEE Region 10 Annual International Conference, Proceedings/TENCON, Institute of Electrical and Electronics Engineers Inc., pp. 997-1000, 2016 IEEE Region 10 Conference, TENCON 2016, Singapore, Singapore, 22/11/16. https://doi.org/10.1109/TENCON.2016.7848155

Automatic quality assessment and segmentation of diabetic retinopathy images. / Kusakunniran, Worapan; Rattanachoosin, Jirat; Sutassananon, Krittanat et al.
Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 997-1000 7848155 (IEEE Region 10 Annual International Conference, Proceedings/TENCON).

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

TY - GEN

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AU - Kusakunniran, Worapan

AU - Rattanachoosin, Jirat

AU - Sutassananon, Krittanat

AU - Anekkitphanich, Phuthimeth

PY - 2017/2/8

Y1 - 2017/2/8

N2 - Diabetes is considered to be one of the most dangerous genetic disorders. It could cause the loss of sight in the case of the diabetic retinopathy. Several medical technologies have been developed and improved to cure the diabetes and overcome the lacks of human experts. This can be done by replacing the manual process using human labors with the automatic diagnosis. In order to achieve this purpose, it requires two main steps which will be focused in this paper. They are the quality assessment and the segmentation of diabetic retinopathy images. In the image quality assessment, four features (namely color, contrast, focus, and illumination) have been investigated. As a result, the contrast histogram in the Principal Component Analysis (PCA) space is used. In the image segmentation, the histogram equalization is used in the pre-processing. Then, the image segmentation based on the iterative selection and the grabcut algorithm is applied. The experimental results demonstrate that the proposed method can achieve very promising performance.

AB - Diabetes is considered to be one of the most dangerous genetic disorders. It could cause the loss of sight in the case of the diabetic retinopathy. Several medical technologies have been developed and improved to cure the diabetes and overcome the lacks of human experts. This can be done by replacing the manual process using human labors with the automatic diagnosis. In order to achieve this purpose, it requires two main steps which will be focused in this paper. They are the quality assessment and the segmentation of diabetic retinopathy images. In the image quality assessment, four features (namely color, contrast, focus, and illumination) have been investigated. As a result, the contrast histogram in the Principal Component Analysis (PCA) space is used. In the image segmentation, the histogram equalization is used in the pre-processing. Then, the image segmentation based on the iterative selection and the grabcut algorithm is applied. The experimental results demonstrate that the proposed method can achieve very promising performance.

KW - automated medical diagnosis

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KW - fundus image

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Kusakunniran W, Rattanachoosin J, Sutassananon K, Anekkitphanich P. Automatic quality assessment and segmentation of diabetic retinopathy images. In Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 997-1000. 7848155. (IEEE Region 10 Annual International Conference, Proceedings/TENCON). doi: 10.1109/TENCON.2016.7848155

Automatic quality assessment and segmentation of diabetic retinopathy images

Abstract

Publication series

Conference

UN SDGs

Keywords

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