A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim

The main purpose of this research is to design a comprehensive algorithm which aims to aid the medical practitioners mainly the radiographers, radiologists and neurologists in primary brain tumors diagnosis. Detection of primary brain tumors is inspired by the necessity of high accuracy as it deals with human life. Presently, various imaging modalities techniques have incarnated as a tool for the doctors and radiologists to help them in diagnosis and treatment domain. While these are highly accurate and fast, they still require experienced and competent medical practitioners for the proper interpretation. Thus, the involvement of information technology is highly demanded in introducing reliable, simple and accurate computer systems. This study presents an algorithm for nosologic segmentation of primary brain tumors on Magnetic Resonance Imaging (MRI) brain images. The MRI technique has been chosen as the digital imaging modality since it provides clearer image for the tissue area as compared to the other techniques that focusing more on bone study such as Computed Tomography (CT) Scan and X-ray. The purpose of segmentation is to highlight the tumor areas, whereas classification is used to identify the type of the primary brain tumors. For this purpose, an algorithm which hybridized the Intensity Based Analysis (IBA), Grey Level Cooccurrence Matrices (GLCM), Adaptive Network-based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) Clustering Algorithm (CAPSOCA) is proposed. The combination of several computer vision techniques was presented which aims to deliver reproducible nosologic segmentation of primary brain tumors which are gliomas and meningiomas…