  Retinal blood vessels structure contains information about diseases like
obesity, diabetes, hypertension and glaucoma. This information is very useful
in identification and treatment of these fatal diseases. To obtain this
information, there is need to segment these retinal vessels. Many kernel based
methods have been given for segmentation of retinal vessels but their kernels
are not appropriate to vessel profile cause poor performance. To overcome this,
a new and efficient kernel based matched filter approach has been proposed. The
new matched filter is used to generate the matched filter response (MFR) image.
We have applied Otsu thresholding method on obtained MFR image to extract the
vessels. We have conducted extensive experiments to choose best value of
parameters for the proposed matched filter kernel. The proposed approach has
examined and validated on two online available DRIVE and STARE datasets. The
proposed approach has specificity 98.50%, 98.23% and accuracy 95.77 %, 95.13%
for DRIVE and STARE dataset respectively. Obtained results confirm that the
proposed method has better performance than others. The reason behind increased
performance is due to appropriate proposed kernel which matches retinal blood
vessel profile more accurately.
