Detail publikace

An Adaptive Threshold Based Algorithm for Detection of Red Lesions of Diabetic Retinopathy in a Fundus Image

Originální název

An Adaptive Threshold Based Algorithm for Detection of Red Lesions of Diabetic Retinopathy in a Fundus Image

Anglický název

An Adaptive Threshold Based Algorithm for Detection of Red Lesions of Diabetic Retinopathy in a Fundus Image

Jazyk

en

Originální abstrakt

The paper proposes an algorithm for detection of Red Lesions present in a fundus image of an eye. Red Lesions include Micro-aneurysms and Hemorrhages, which are the symptoms of Diabetic Retinopathy, a widespread eye disease which affects almost every diabetic patient at some point of the patients life. The paper presents an adaptive method to detect the red lesions present in an image. The proposed method will estimate the upper threshold and the lower threshold of the red lesions for the given fundus image individually based on local image information. The significance of the adaptive nature of this proposed algorithm is that fundus images acquired from different cameras may vary in quality and resolution. As a result the intensity of red lesions may vary from image to image. Since, the intensity of red lesions is similar to that of the blood vessels for a specific image, therefore this similarity has been utilized to develop an accurate, adaptive algorithm for the detection of red lesions, wherein every fundus image is processed with a different intensity threshold value resulting more accurate detection.

Anglický abstrakt

The paper proposes an algorithm for detection of Red Lesions present in a fundus image of an eye. Red Lesions include Micro-aneurysms and Hemorrhages, which are the symptoms of Diabetic Retinopathy, a widespread eye disease which affects almost every diabetic patient at some point of the patients life. The paper presents an adaptive method to detect the red lesions present in an image. The proposed method will estimate the upper threshold and the lower threshold of the red lesions for the given fundus image individually based on local image information. The significance of the adaptive nature of this proposed algorithm is that fundus images acquired from different cameras may vary in quality and resolution. As a result the intensity of red lesions may vary from image to image. Since, the intensity of red lesions is similar to that of the blood vessels for a specific image, therefore this similarity has been utilized to develop an accurate, adaptive algorithm for the detection of red lesions, wherein every fundus image is processed with a different intensity threshold value resulting more accurate detection.

Dokumenty

BibTex


@inproceedings{BUT110427,
  author="Shaunak {Ganguly} and Shaumik {Ganguly} and Kshitij {Srivastava} and Malay Kishore {Dutta} and M. {Parthasarathi} and Kamil {Říha} and Radim {Burget}",
  title="An Adaptive Threshold Based Algorithm for Detection of Red Lesions of Diabetic Retinopathy in a Fundus Image",
  annote="The paper proposes an algorithm for detection of Red Lesions present in a fundus image of an eye. Red Lesions include Micro-aneurysms and Hemorrhages, which are the symptoms of Diabetic Retinopathy, a widespread eye disease which affects almost every diabetic patient at some point of the patients life. The paper presents an adaptive method to detect the red lesions present in an image. The proposed method will estimate the upper threshold and the lower threshold of the red lesions for the given fundus image individually based on local image information. The significance of the adaptive nature of this proposed algorithm is that fundus images acquired from different cameras may vary in quality and resolution. As a result the intensity of red lesions may vary from image to image. Since, the intensity of red lesions is similar to that of the blood vessels for a specific image, therefore this similarity has been utilized to develop an accurate, adaptive algorithm for the detection of red lesions, wherein every fundus image is processed with a different intensity threshold value resulting more accurate detection.",
  booktitle="MEDCOM 2014 CD-ROM",
  chapter="110427",
  doi="10.1109/MedCom.2014.7005982",
  howpublished="electronic, physical medium",
  year="2015",
  month="november",
  pages="91--94",
  type="conference paper"
}