Accuracy evaluation of brain tumor detection using entropy-based image thresholding
Other Title(s)
دقة النهج القائم على الانتروبيا من خلال العتبة للكشف عن الورم الدماغي
Dissertant
Thesis advisor
Comitee Members
al-Sawadi, Hamzah Abbas
al-Mubayyidin, Wisam
University
Middle East University
Faculty
Faculty of Information Technology
Department
Department of Computer Information Systems
University Country
Jordan
Degree
Master
Degree Date
2017
English Abstract
Image thresholding is one of the techniques that are used for image segmentation.
Threshold techniques divide the image into two main regions, these are: Foreground and Background.
The output of the thresholding process is a binary image with only two regions that are formed by the highest possible contrast that could be found in the image.
Entropies are information gain approaches that have been used for image thresholding with various application and image modalities.
However, the accuracy of the existing entropies for image thresholding has been studied in general domain (e.g.: natural images)that teams from the regular medical images and images that form in the ordinary image is a reflection of light objects, While medical images.
Taken by magnetic resonance imaging, for example, A strong magnetic field is used with radio frequencies and computer to produce automatic selection of the best result.
It produces the results with the highest accuracy.
detailed images of organs and soft tissues, bones and other internal parts of the body.
and were not compared thoroughly.
In this work, the accuracy of the entropy-based thresholding approaches and their combination in brain tumor detection framework is investigated.
For this purpose, a framework for brain tumor segmentation is developed.
The developed framework is made simple and has the core process of the image thresholding, in order to evaluate the accuracy of the entropies.
Five entropies, namely, Reniyh, Maximum, Minimum, Tsallis and Kapur are evaluated.
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
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
83
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Background and literature review.
Chapter Three : Proposed work.
Chapter Four : Experimental results.
Chapter Five : Conclusion and future work.
References.
American Psychological Association (APA)
al-Yahya, Amal Qasim Ali. (2017). Accuracy evaluation of brain tumor detection using entropy-based image thresholding. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-762710
Modern Language Association (MLA)
al-Yahya, Amal Qasim Ali. Accuracy evaluation of brain tumor detection using entropy-based image thresholding. (Master's theses Theses and Dissertations Master). Middle East University. (2017).
https://search.emarefa.net/detail/BIM-762710
American Medical Association (AMA)
al-Yahya, Amal Qasim Ali. (2017). Accuracy evaluation of brain tumor detection using entropy-based image thresholding. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-762710
Language
English
Data Type
Arab Theses
Record ID
BIM-762710
