Work place: Middle East University/Faculty of Information Technology, Amman, Jordan
Research Interests: Computational Science and Engineering, Computational Engineering, Engineering
Amal Q. Alyahya is currently a PhD student at The University of Jordan. She received her first degree in computer engineer from Fahad Bin Sultan University, Saudi Arabia in 2014 and her master degree in Computer Science from Middle East University, Jordan.
DOI: https://doi.org/10.5815/ijitcs.2018.03.02, Pub. Date: 8 Mar. 2018
In this paper, the accuracy of the entropy-based thresholding approaches in brain tumor detection framework is investigated. Entropies are information gain methods that have been used for image thresholding with various application and different image modalities. The accuracy of the existing entropies for image thresholding has been studied in general domain (e.g.: natural images) and were not compared thoroughly. Thus, a framework for brain tumor segmentation is proposed with the core process of the image thresholding, in order to evaluate the accuracy of the entropies. Five entropies, namely, Renyi, Maximum, Minimum, Tsallis and Kapur are evaluated. Moreover, the aggregation of entropies was implemented and evaluated. The results show that the maximum entropy is the best for brain tumor detection. Moreover, it was shown that aggregation of entropies output does not enhance the result, however, it works as automatic selection of the best result and produces the results with the highest accuracy.[...] Read more.
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