Brain Tumor Detection Features Extraction From MR Images Using Segmentation, Image Optimization Classification Techniques

Abstract

Nervous system most vital part is brain. Our actions whether voluntary or involuntary all depend on it. The health of the brain is very crucial. A number of deaths are caused by brain related diseases. Brain Tumors are one of the dangerous diseases that damages the brain and is sometimes incurable. Tumors are formed when brain cells split up in an abnormal fashion for various reasons. Timely identification of brain tumors is very crucial for curing this disease. Many techniques are developed by researchers for diagnosis of brain tumors with the help of X-Ray, CT Scan and Magnetic Resonance Imaging (MRI), however MRI scan is considered to be an optimal solution for brain tumor identification due to its harmless effects on human body. Examination of brain tumor tissues is very complex and without clear detailed study by radiologist can lead to loss of precious human life. Moreover manual diagnosis of MR images is more prone to human errors. Due to these issues-computer aided diagnosis using efficient algorithms are favoured over manual diagnosis of brain tumors. In biomedical engineering, various methods have been employed for detection, segmentation and classification of brain tumor, but they contain various shortcomings in one form or other. This paper is focused on evaluating the dataset of brain MRIs and are enhanced by employing various image processing and segmentation techniques before classifying the MR image in tumor and non-tumor images. Furthermore-performance of popular classification techniques such as SVM, KNN and ANN are evaluated by training and testing them with same set of MR images. The output results are computed by statistical analysis.