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International Journal of Computer Trends and Technology (IJCTT) – volume 7 number 1– Jan 2014
Early Proliferation Stage of Detecting Diabetic Retinopathy Using
Bayesian Classifier Based Level Set Segmentation
S.Vijayalakshmi1, P.Sivaprakasam2
1
(Research Scholar, Karpagam University,Coimbatore, India)
(Department of MCA, Park College of Engineering and Technology, Coimbatore, India)
2
(Department of Computer Science, Associate Professor Sri Vasavi College, Erode, India)
1
ABSTRACT : This article presents Bayesian
Classifier which controls the levels set segmentation and it detect the retinal clots at an early stage from the image captured from fundus camera. The classifier is a probabilistic and used for the control of level set contour propagation for the detection of class clot defined, extracting the retinal vessels even with minute deformation due to the clots. The algorithm is tested in MATLAB on fundus images taken at various stages of progression and results which proves the effectiveness of the proposed method.
Keywords -Bayesian Classifier, Blood Clots,
Contour Propagation, Diabetic Retina, Level set segmentation 1.
INTRODUCTION
A diabetic retinopathy is the most common diabetic eye disease which leads to the blindness because of the changes in the blood vessels of the retina. In this disease, blood vessels may swell, leak fluid and abnormal new growth on the surface of the retina. The retina is the light-sensitive tissue at the back of the eye. A healthy retina is necessary for good vision. Figure 1 shows the diabetic retina with swollen nerves. This makes the vision impaired and lead to permanent blindness at the end stages.
Fig 1: A diabetic retinal image taken from a fundus camera
ISSN: 2231-2803
To detect the diabetic retina at an early stage it is important to adopt an enhanced image processing tool for the detection of even small clots for an early diagnosis. Atul Kumar[2] identified
Early Proliferation Stage of Detecting Diabetic Retinopathy Using
Bayesian Classifier Based Level Set Segmentation
S.Vijayalakshmi1, P.Sivaprakasam2
1
(Research Scholar, Karpagam University,Coimbatore, India)
(Department of MCA, Park College of Engineering and Technology, Coimbatore, India)
2
(Department of Computer Science, Associate Professor Sri Vasavi College, Erode, India)
1
ABSTRACT : This article presents Bayesian
Classifier which controls the levels set segmentation and it detect the retinal clots at an early stage from the image captured from fundus camera. The classifier is a probabilistic and used for the control of level set contour propagation for the detection of class clot defined, extracting the retinal vessels even with minute deformation due to the clots. The algorithm is tested in MATLAB on fundus images taken at various stages of progression and results which proves the effectiveness of the proposed method.
Keywords -Bayesian Classifier, Blood Clots,
Contour Propagation, Diabetic Retina, Level set segmentation 1.
INTRODUCTION
A diabetic retinopathy is the most common diabetic eye disease which leads to the blindness because of the changes in the blood vessels of the retina. In this disease, blood vessels may swell, leak fluid and abnormal new growth on the surface of the retina. The retina is the light-sensitive tissue at the back of the eye. A healthy retina is necessary for good vision. Figure 1 shows the diabetic retina with swollen nerves. This makes the vision impaired and lead to permanent blindness at the end stages.
Fig 1: A diabetic retinal image taken from a fundus camera
ISSN: 2231-2803
To detect the diabetic retina at an early stage it is important to adopt an enhanced image processing tool for the detection of even small clots for an early diagnosis. Atul Kumar[2] identified
References: region criteria, Biomedical Imaging: From Nano to Macro, 2009