A Novel Clip Limit Estimation Technique for Blood Vessel Segmentation and OD/OS Classification Technique for Retina Images

Abstract

The evolution of digital health care system in medical imaging has become a contemporary area. Human eye contain numerous nerves and sensitive tissues in it which are highly prone to eye diseases like hemorrhage, exudates, micro aneurysms etc. These eye diseases may affect blood vessels of retina to a greater extent. There are different research contributions made in this research work. They are (i) Identification of OD/OS (Right eye / Left eye) using DWT. (ii) A clip limit estimation technique which identifies the suitable clip limit value for enhancement. (iii) A framework for blood vessel extraction using estimated clip limit value and thresholding technique. Using DWT the localization of optic disc is done. The clip limit estimation technique involves various analysis to estimate the suitable clip limit for enhancement namely qualitative analysis, quantitative analysis, intensity distribution analysis and statistical analysis. A framework is proposed for retina blood vessel segmentation using clip limit estimation technique. The performance of the proposed methods are measured in terms of classification accuracy, sensitivity, specificity. The framework is tested on different datasets which produced an accuracy of 96.87% on DRIVE dataset, 96.89% on HRF dataset, 97.34% on STARE dataset, and 97.52% on DIARETDB1 dataset respectively.

Keywords:
Oculus Sinister (OS); Oculus Dexter (OD); Clip Limit; Clipping Process; Blood vessels; Retina

HIGHLIGHTS

• Technique for identification of OD/OS using DWT is proposed.

• Clip limit estimation using different analytical strategies is proposed.

• A framework for blood vessel extraction is presented.

• The proposed techniques are tested on DRIVE, HRF, STARE, DIARETDB1 and VAMPIRE datasets.

• The proposed technique is compared with existing techniques.