The Investigation and Analysis of Line Junction Detection in Biomedical Images
DOI:
https://doi.org/10.48314/ceti.v1i3.36Keywords:
Directional filter, Gabor filter, Gaussian filter, Line detectionAbstract
Line junction detection plays a vitaltask in the segmentation of biomedical images in various applications such as liver blood vessel detection, diabetic retinopathy, neuron reconstruction studies, etc. Previous line junction techniques hugely depend upon skeletonization and image segmentation. In this paper, we present line junction detection based on three kinds of filters such as Gaussian filter, directional filter, Gabor filter,and Histogram of Oriented Gradient (HOG) employed for the line junction score measurement, ridge forks and branches detection, ridge point detection and junction strength detection respectively. We have conducted extensive experimentation on the DRIVE retinal fundus image database. The proposed algorithm's performance is evaluated based on qualitative and quantitative analysis, and it is observed that the proposed technique outperforms traditional approaches. It results in an averageaccuracy, precision, recall and F1-score of 96.60%, 92.50%, 94.08 and 95.40% for line junction detection on DRIVE dataset.
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