You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly.
Go to headerGo to navigationGo to searchGo to contentsGo to footer
In content section. Select this link to jump to navigation

Analysis of nervous fiber, muscle, and blood vessels using their ulraviolet near infrared reflectance characteristics

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Tufan, Kadira; * | Belli, Ahmet Korkutb

Affiliations: [a] Computer Engineering Department, Fatih University, Istanbul, Turkey | [b] Department of General Surgery, Mugla Sitki Kocman Medical School, Mugla, Turkey

Correspondence: [*] Address for correspondence: Kadir Tufan, Chairman of Computer Engineering Department, Fatih University, 34500, Buyukcekmece, Istanbul, Turkey. Tel.: +90 212 866 33 00; Fax: +90 212 866 34 12; E-mail: [email protected].

Keywords: Nervous tissue, light reflectance, surgery, support vector machines

DOI: 10.3233/BME-151523

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S2179-S2185, 2015

Published: 2015
Get PDF open access

Injury to the nervous system can lead to irreversible problems as nervous tissues have limited regenerative capability. Therefore it is imperative to find an objective, reliable, cheap, and easy-to-apply method that separates nervous fibers from muscles and blood vessels. The aim of this study is to determine structural differences that can aid in easy and reliable identification of nervous fibers. We analyzed light reflectance from these tissues from 230 nm to 1000 nm and found that in the range of 400 nm-600 nm nervous fibers have higher reflectance in comparison to others. Therefore, we generated distinct features in this range and utilized support vector machine to automatically classify samples. Classification performance demonstrated that light reflectance is a good candidate feature that can help to classify nervous tissue.

Show more