Accuracy evaluation of brain tumor detection using entropy-based image thresholding

العناوين الأخرى

دقة النهج القائم على الانتروبيا من خلال العتبة للكشف عن الورم الدماغي

مقدم أطروحة جامعية

al-Yahya, Amal Qasim Ali

مشرف أطروحة جامعية

Abu Shurayhah, Ahmad Adil

أعضاء اللجنة

al-Sawadi, Hamzah Abbas
al-Mubayyidin, Wisam

الجامعة

جامعة الشرق الأوسط

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم نظم المعلومات الحاسوبية

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2017

الملخص الإنجليزي

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

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• الدماغ
• الأورام
• القصور الحراري

عدد الصفحات

83

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-762710