Abstract:
The focus of this thesis is to report an automated, efficient, and robust method of brain tumor detection and classification from Magnetic Resonance Images (MRI) images. Clinically, it is a challenging issue faced by the researchers working in this domain. In routine health care units, Magnetic Resonance (MR) scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neuro-oncologists. Due to huge volume of brain anatomical data produced by MRI scanners, it is almost impossible to manually analyze every slice. Conclusively, if automated protocols are executed for auto-interpretation; not only the radiologist will be assisted but also a better pathological assessment process would be expected. Several methods have been suggested to address this problem, but still, accuracy, robustness and optimization is still an open issue to address. The development of such automated procedures is difficult due to complex organization of brain cells, several types of tumor, difference in medical traits of a specific ethnicity and many more factors. To achieve the target, research has been started from reviewing the most popular and prominent state-of-the-art methods. Based upon the reviewed literature, automated brain tumor detection and classification techniques have been reported with high computational cost, low classification rates, detection and classification of only one or a few of brain tumor types, lack of robustness, etc. Therefore, step wise research and experiments based upon empirical scientific methodology have been performed in order to achieve the objectives of brain tumor classification.
