CAPSOCA: hybrid technique for nosologic segmentation of primary brain tumors - Shafaf Ibrahim; Noor Elaiza Abd Khalid; Mazani Manaf (Institute of Advanced Engineering and Science, 2019)
Detection of primary brain tumors is inspired by the necessity of high accuracy as it deals with human life. Various imaging modalities techniques have incarnated as a tool in diagnosis and treatment domain. Yet, experienced and competent medical practitioners for the proper interpretation are still required. Thus, the involvement of information technology is highly demanded in introducing reliable and accurate computer systems. This study presents an algorithm for nosologic segmentation of primary brain tumors on Magnetic Resonance Imaging (MRI) brain images. Nosologic refers to the classification of diseases that can facilitate the diagnosis of neurological diseases. 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 Grey Level Co- occurrence Matrices (GLCM), Intensity Based Analysis (IBA), Adaptive Network-based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) Clustering Algorithm (CAPSOCA) is proposed. The combination of several computer vision techniques is aim to deliver reproducible nosologic segmentation of primary brain tumors which are gliomas and meningiomas. The performance of the CAPSOCA is quantified by two measurements which are segmentation and classification accuracy. The segmentation accuracy is evaluated using comparison with ground truth approach. On the other hand, the classification accuracy is quantified using a truth table by comparing the classification outcomes with histopathology diagnosis. Upon the testing conducted, the CAPSOCA was proven to be an effective algorithm for nosologic segmentation of primary brain tumors. It appeared to return 88.09% of overall mean accuracy for gliomas segmentation, 86.92% of overall mean accuracy for meningiomas segmentation. In another note, 83.72% and 85.19% of classification accuracy for gliomas and meningiomas were observed.