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Adaptive techniques for brain tumor detection
Other Title(s)
تقنيات مطورة للكشف عن أورام الدماغ
Dissertant
Thesis advisor
Ali, Salih Mahdi
Abbud, Luayy Kazim
University
University of Baghdad
Faculty
College of Science
Department
Department of Physics
University Country
Iraq
Degree
Ph.D.
Degree Date
2014
English Abstract
Brain tumors are one of the major causes of death among people.
The chances of survival can be increased if the tumor is detected correctly at its early stage.
Detection of tumors in MRI of brain is not an easy task when the tumor is overlapped with dense brain tissues.
Despite numerous efforts and acceptable results in brain tumors segmentation, accurate and reproducible segmentation and characterization of abnormalities still a challenging task due to the varieties in tumor shapes, locations and the change in intensities of various types of tumors.
The aim of the current study is to introduce an adaptive global automatic technique that can adequately process all slices of Magnetic Resonance Imaging (MRI) of brain; i.e.
detecting, isolating and extracting the tumor regions especially when only a single source of information is available (e.g.
gray tone MR Images).
Many segmentation methods including clustering algorithms, region growing and artificial neural networks of both schemes of training supervised and unsupervised were suggested and used to achieve optimal separation of brain's tissues, which leads to better isolation of the abnormalities (tumors).
To extract the brain's matter; improved skull stripped methods depending on traditional and non-traditional methods were proposed; i.e.
skull stripping utilizing contour filling method, deformable contour algorithm based on traditional and active contour, modified contour technique based on KMeans clustered image and skull stripping utilizing morphological operations and active contour.
The final results were promising compared with the common known methods.
The most important factor that affects the results of skull stripping methods was the adopted dataset.
In tumor isolation, the proposed methods were: contour based segmentation utilizing contour and contour filling methods, K-Means based on Fuzzy C-Mean (FCM), K-Means and FCM based on intensity and location, K-Means and FCM based on Gray Level Cooccurrence Matrix (GLCM) features, Possibilistic Fuzzy C-Mean based on image histogram.
Untraditional methods like hierarchical self-organization feature map, and hierarchical self-organization feature map based on FCM.
Moreover, supervised neural networks based on image intensity and standard deviation once, and based on GLCM features another are also used to modify the overall efficiency suitable for the work configuration, which is a very hard case of segmentation due to the overlapping spectrum fingerprint of tissues.
The results of these methods were acceptable.
Increasing the used extracted features (information) improves the quality to a certain limit (especially when the features gained from single source of data), but when implementing several sources the results showed better quality.
According to the radiologist consultations the K-Means and K-Means based on intensity and location techniques have yield more accurate than fuzzy methods.
The general conclusion is that, there is no one global automatic technique suitable for tumor detection in all slices due to the differences of these slices.
Main Subjects
No. of Pages
298
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : General introduction.
Chapter Two : Magnetic resonance imaging and brain tumors.
Chapter Three : Medical image segmentation techniques.
Chapter Four : Methodologies and results.
Chapter Five : Conclusions and suggestions for future work.
References.
American Psychological Association (APA)
Abdun, Rabab Sadun. (2014). Adaptive techniques for brain tumor detection. (Doctoral dissertations Theses and Dissertations Master). University of Baghdad, Iraq
https://search.emarefa.net/detail/BIM-741082
Modern Language Association (MLA)
Abdun, Rabab Sadun. Adaptive techniques for brain tumor detection. (Doctoral dissertations Theses and Dissertations Master). University of Baghdad. (2014).
https://search.emarefa.net/detail/BIM-741082
American Medical Association (AMA)
Abdun, Rabab Sadun. (2014). Adaptive techniques for brain tumor detection. (Doctoral dissertations Theses and Dissertations Master). University of Baghdad, Iraq
https://search.emarefa.net/detail/BIM-741082
Language
English
Data Type
Arab Theses
Record ID
BIM-741082