• Muskan Tembhare
• Prof. Mahendra Sahare
• Prof. Anurag Srivastava

Retinal blood vessel segmentation and analysis is critical for the computer-aided diagnosis of different diseases such as diabetic retinopathy. This study presents an automated deep learning method for segmenting the retinal vasculature based on optimized methods. The algorithm initially applies a preprocessing step using morphological operators to enhance the vessel tree structure against a non-uniform image background. The main pro-cessing applies the adaptive histogram equalization, followed by vessel validation, vessel refinement and vessel reconstruction to achieve the final segmentation. The method was tested on three publicly available datasets. Segmentation performance was evaluated using three measures: accuracy, receiver operating characteristic (ROC) analysis, and the structural similarity index. ROC analysis resulted in area under curve values of 97.39%, 97.01%, and 97.12%, for the DRIVE, , respectively. Also, the results of accuracy were 0.9688, 0.9646, and 0.9475 for the same datasets.

Retinal Blood, Segmentation, Deep Learning, CNN

"Feature Optimization Based Retinal Vessel Segmentation Using Deep Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.g610-g617, November-2024, Available :http://www.jetir.org/papers/JETIR2411662.pdf

"Feature Optimization Based Retinal Vessel Segmentation Using Deep Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppg610-g617, November-2024, Available at : http://www.jetir.org/papers/JETIR2411662.pdf