Highly accurate blood vessel segmentation using texture-based modified K-means clustering with deep learning model

This paper suggests a blood vessel segmentation using the Texture-Based Modified K-means Clustering (TBMKC) technique to lessen the detrimental effects of light lesions. In the suggested method, the K value is chosen automatically by maximizing the local pixel data of the input image. Utilizing the Gabor Filter and Scale-Invariant Feature Transform (SIFT), respectively, texture and statistical characteristics are used to extract the local information. The number of pixels present in each class is used to alter the cluster centroid using Particle Swarm Optimization (PSO) methods. Local adaptive thresholding is then used to separate the blood vessel pattern. The proposed method is invariant to scales and rotations due to SIFT features. The thresholding output is given to the modified DCNN (Deep Convolutional Neural Network). Finally, the performance of the (MDCNN+ TBMKC+ PSO) scheme is computed over other extant schemes. The outcomes of the adopted scheme is determined and tested using the retinal image from DRIVE, STARE, HRF, and CHASE_DBI databases. The accuracy of the proposed method is 13.41%, 7.22%, 9.56%, 6.24%, 2.35%, 14.54%, 13.23%, 3.71%, 10.82%, 8.28%, 7.98%, and 54.01% better than the existing method like M-GAN, MCET-HHO, CNN, DBN, SVM, RNN, NN, MDCNN+PSO, MDCNN+GA, MCNN+FF, MDCNN+ABC, and MDCNN+LA.