Detail publikace

Automatic Detection of Red Lesions in Diabetic Retinopathy using Shape Based Extraction Technique in Fundus Image

Originální název

Automatic Detection of Red Lesions in Diabetic Retinopathy using Shape Based Extraction Technique in Fundus Image

Anglický název

Automatic Detection of Red Lesions in Diabetic Retinopathy using Shape Based Extraction Technique in Fundus Image

Jazyk

en

Originální abstrakt

The paper proposes an automatic image processing algorithm based on shape features for the detection of red lesions. In Diabetic Retinopathy Micro-aneurysms and hemorrhages comes under the category of red lesions, which is the most common eye disease caused in diabetic patients and also leads to blindness. This paper describes an effective methodology to study any computer-aided fundus image that can be utilized as a tool for diagnosis and detection of red lesions. A Shape based extraction technique using three parameters i.e. Perimeter Area and Eccentricity is used to segment out the red lesions from rest of the image. Since the algorithm consider shape features for detection of red lesion which makes it efficient and independent of image quality. The results that are experimentally obtained by applying this algorithm has been compared with those of the ophthalmologist and the comparison of these results have been highly accurate. In addition to the accuracy of the obtained results, the proposed method is fast and having very low computational time.

Anglický abstrakt

The paper proposes an automatic image processing algorithm based on shape features for the detection of red lesions. In Diabetic Retinopathy Micro-aneurysms and hemorrhages comes under the category of red lesions, which is the most common eye disease caused in diabetic patients and also leads to blindness. This paper describes an effective methodology to study any computer-aided fundus image that can be utilized as a tool for diagnosis and detection of red lesions. A Shape based extraction technique using three parameters i.e. Perimeter Area and Eccentricity is used to segment out the red lesions from rest of the image. Since the algorithm consider shape features for detection of red lesion which makes it efficient and independent of image quality. The results that are experimentally obtained by applying this algorithm has been compared with those of the ophthalmologist and the comparison of these results have been highly accurate. In addition to the accuracy of the obtained results, the proposed method is fast and having very low computational time.

BibTex


@inproceedings{BUT127770,
  author="Aman {Pandey} and Ritu {Chandra} and Malay Kishore {Dutta} and Radim {Burget} and Václav {Uher} and Jiří {Minář}",
  title="Automatic Detection of Red Lesions in Diabetic Retinopathy using Shape Based Extraction Technique in Fundus Image",
  annote="The paper proposes an automatic image processing algorithm based on shape features for the detection of red
lesions. In Diabetic Retinopathy Micro-aneurysms and hemorrhages comes under the category of red lesions, which is the most common eye disease caused in diabetic patients and also leads to blindness. This paper describes an effective methodology to study any computer-aided fundus image that can be utilized as a tool for diagnosis and detection of red lesions. A Shape based extraction technique using three parameters i.e. Perimeter Area and Eccentricity is used to segment out the red lesions from rest of the image. Since the algorithm consider shape features for
detection of red lesion which makes it efficient and independent of image quality. The results that are experimentally obtained by applying this algorithm has been compared with those of the ophthalmologist and the comparison of these results have been highly accurate. In addition to the accuracy of the obtained results, the proposed method is fast and having very low computational time.",
  booktitle="39th International Conference on Telecommunications and Signal Processing (TSP) 2016",
  chapter="127770",
  howpublished="electronic, physical medium",
  year="2016",
  month="june",
  pages="538--542",
  type="conference paper"
}