Robust object extraction and change detection in retinal images for diabetic clinical studies

Qin Li; Jia You; Lei Zhang; Prabir Bhattacharya

doi:10.1109/CIISP.2007.369195

Robust object extraction and change detection in retinal images for diabetic clinical studies

Qin Li
, Jia You
, Lei Zhang
, Prabir Bhattacharya

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

With the rapid advances in computing and electronic imaging technology, there has been increasing interest in developing computer aided medical diagnosis systems to improve the medical service for the public. Images of ocular fundus provide crucial observable features for diagnosing many kinds of pathologies such as diabetes, hypertension, and arteriosclerosis. A computer-aided retinal image analysis system can help eye specialists to screen larger populations and produce better evaluation of treatment and more effective clinical study. This paper is focused on the immediate needs for clinical studies on diabetic patients. Our system includes multiple feature extraction, robust retinal vessel segmentation, hierarchical change detection and classification. The output throughout this system will assist doctors to speed up screening large populations for abnormal cases, and facilitate evaluation of treatment for clinical study.

Original language	English
Title of host publication	Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007
Pages	357-362
Number of pages	6
DOIs	https://doi.org/10.1109/CIISP.2007.369195
Publication status	Published - 25 Sept 2007
Event	2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007 - Honolulu, HI, United States Duration: 1 Apr 2007 → 5 Apr 2007

Conference

Conference	2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007
Country/Territory	United States
City	Honolulu, HI
Period	1/04/07 → 5/04/07

UN SDGs

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

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Artificial Intelligence
Signal Processing
Software
Electrical and Electronic Engineering

More information

10.1109/CIISP.2007.369195

Cite this

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title = "Robust object extraction and change detection in retinal images for diabetic clinical studies",

abstract = "With the rapid advances in computing and electronic imaging technology, there has been increasing interest in developing computer aided medical diagnosis systems to improve the medical service for the public. Images of ocular fundus provide crucial observable features for diagnosing many kinds of pathologies such as diabetes, hypertension, and arteriosclerosis. A computer-aided retinal image analysis system can help eye specialists to screen larger populations and produce better evaluation of treatment and more effective clinical study. This paper is focused on the immediate needs for clinical studies on diabetic patients. Our system includes multiple feature extraction, robust retinal vessel segmentation, hierarchical change detection and classification. The output throughout this system will assist doctors to speed up screening large populations for abnormal cases, and facilitate evaluation of treatment for clinical study.",

author = "Qin Li and Jia You and Lei Zhang and Prabir Bhattacharya",

year = "2007",

month = sep,

day = "25",

doi = "10.1109/CIISP.2007.369195",

language = "English",

isbn = "1424407079",

pages = "357--362",

booktitle = "Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007",

note = "2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007 ; Conference date: 01-04-2007 Through 05-04-2007",

}

Li, Q, You, J , Zhang, L & Bhattacharya, P 2007, Robust object extraction and change detection in retinal images for diabetic clinical studies. in Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007., 4221445, pp. 357-362, 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007, Honolulu, HI, United States, 1/04/07. https://doi.org/10.1109/CIISP.2007.369195

Robust object extraction and change detection in retinal images for diabetic clinical studies. / Li, Qin; You, Jia ; Zhang, Lei et al.
Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007. 2007. p. 357-362 4221445.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N2 - With the rapid advances in computing and electronic imaging technology, there has been increasing interest in developing computer aided medical diagnosis systems to improve the medical service for the public. Images of ocular fundus provide crucial observable features for diagnosing many kinds of pathologies such as diabetes, hypertension, and arteriosclerosis. A computer-aided retinal image analysis system can help eye specialists to screen larger populations and produce better evaluation of treatment and more effective clinical study. This paper is focused on the immediate needs for clinical studies on diabetic patients. Our system includes multiple feature extraction, robust retinal vessel segmentation, hierarchical change detection and classification. The output throughout this system will assist doctors to speed up screening large populations for abnormal cases, and facilitate evaluation of treatment for clinical study.

AB - With the rapid advances in computing and electronic imaging technology, there has been increasing interest in developing computer aided medical diagnosis systems to improve the medical service for the public. Images of ocular fundus provide crucial observable features for diagnosing many kinds of pathologies such as diabetes, hypertension, and arteriosclerosis. A computer-aided retinal image analysis system can help eye specialists to screen larger populations and produce better evaluation of treatment and more effective clinical study. This paper is focused on the immediate needs for clinical studies on diabetic patients. Our system includes multiple feature extraction, robust retinal vessel segmentation, hierarchical change detection and classification. The output throughout this system will assist doctors to speed up screening large populations for abnormal cases, and facilitate evaluation of treatment for clinical study.

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DO - 10.1109/CIISP.2007.369195

M3 - Conference article published in proceeding or book

SN - 1424407079

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BT - Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007

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Y2 - 1 April 2007 through 5 April 2007

ER -

Robust object extraction and change detection in retinal images for diabetic clinical studies

Abstract

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UN SDGs

ASJC Scopus subject areas

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