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Blood Vessel Segmentation in Fundus Images and Detection of Glaucoma using SVM

Blood Vessel Segmentation in Fundus Images and Detection of Glaucoma using SVM

ABSTRACT

Automated blood vessel segmentation plays an important role in the diagnosis and treatment of various cardiovascular and ophthalmologic diseases. In existing, blood vessel segmentation and glaucoma detection is proposed based on the ISNT ratio. It demonstrated the rate of accuracy is less (85.7%). A method based on mean filtering and morphological operation is introduced in the proposed method for vessel segmentation. After that feature extraction and classification is introduced for glaucoma detection. Here the glaucoma is detected by using SVM classifier. The analysis is performed on Glaucomatous and normal eye. The advantage of the proposed method is that by calculating the area of blood vessels in whole eye, glaucoma can be detected with high accuracy (92.85%) in a simpler manner.

PROJECT OUTPUT VIDEO:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram :2 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

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