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Matrix Mask Overlapping and Convolution Eight Directions for Blood Vessel Segmentation on Fundus Retinal Image

Journal from gdlhub / 2016-11-14 05:49:31
Oleh : Matrix Mask Overlapping and Convolution Eight Directions for Blood Vessel Segmentation on Fundus Retinal Image, Telkomnika
Dibuat : 2016-09-01, dengan 1 file

Keyword : Fundus Image, segmentation, 8 directions Convolution, overlapping
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/97

Diabetic Retinopathy is one of the diseases that have the effect of a high mortality rate after heart disease and cancer. However, the disease can be early detected through blood vessels and the optic nerve head in Fundus images. Blood vessels separation of the optic nerve head required high effort when it is conducted manually, therefore it is necessary that the appropriate method to perform segmentation of the object. Level Set method is well-known as object segmentation method based on object deformable. However, the methods have the disadvantage; it requires initialization before the segmentation process. In this research, segmentation method without initialization process is proposed. The segmentation is conducted by using the maximum value selection results of convolution 8 directions. Experimental results show that, proposed method has obtained 89.48% accuracy. Segmentation errors are caused by small branches, where they are not connected, so that the objects are supposed as noises.

Deskripsi Alternatif :

Diabetic Retinopathy is one of the diseases that have the effect of a high mortality rate after heart disease and cancer. However, the disease can be early detected through blood vessels and the optic nerve head in Fundus images. Blood vessels separation of the optic nerve head required high effort when it is conducted manually, therefore it is necessary that the appropriate method to perform segmentation of the object. Level Set method is well-known as object segmentation method based on object deformable. However, the methods have the disadvantage; it requires initialization before the segmentation process. In this research, segmentation method without initialization process is proposed. The segmentation is conducted by using the maximum value selection results of convolution 8 directions. Experimental results show that, proposed method has obtained 89.48% accuracy. Segmentation errors are caused by small branches, where they are not connected, so that the objects are supposed as noises.

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