The Investigation and Analysis of  Line Junction Detection in Biomedical Images

Authors

  • Seyyed Ahmad Edalatpanah * Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran. https://orcid.org/0000-0001-9349-5695
  • Natalja Osintsev Fraunhofer-Institut für Holzforschung Wilhelm-Klauditz Institut WKI, Bienroder Weg 54 E, Brunswick, Germany.
  • Hamiden Abd El-Wahed Khalifa Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt. https://orcid.org/0000-0002-8269-8822
  • Amanna Ghanbari Talouki Department of Technical and Engineering, Ayandegan Institute of Higher Education, Tonekabon, Mazandaran, Iran. https://orcid.org/0000-0001-5326-4075

https://doi.org/10.48314/ceti.v1i3.36

Abstract

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.

Keywords:

Directional filter, Gabor filter, Gaussian filter, Line detection

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Published

2024-08-25

Issue

Vol. 1 No. 3 (2024): Computational Engineering and Technology Innovations

Section

Articles

How to Cite

Edalatpanah, S. A., Osintsev, N., Abd El-Wahed Khalifa, H., & Ghanbari Talouki , A. (2024). The Investigation and Analysis of  Line Junction Detection in Biomedical Images. Computational Engineering and Technology Innovations, 1(3), 160-169. https://doi.org/10.48314/ceti.v1i3.36

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