An Efficient Computer Aided Detection for 3D Neurostructural Reconstruction of Magnetic Resonance Images

Abstract

The comprehensive framework for analyzing brain images performs by the integration between three dimensional (3D) reconstruction and neuroimaging approach to realize brain diseases progressions. Computer Aided Detection (CAD) technology has numerous achievements in brain tumor processing for improving the quality of brain visualization to support neuroradiologists without the need for surgical biopsy or resection. Despite the advance in the radiological diagnosis of neuroimaging data, magnetic resonance imaging (MRI) has some restrictions that related to human errors and incomplete interpretation of brain tumor regions. Also, MRI scan produces 2D images of the brain that was very difficult to handle different types of tumor. Therefore, many algorithms are used computer-based classification to accurately distinguish between tumor regions from the brain MR images that provided an early diagnosis of brain diseases. This study investigated the CAD system using 3D image reconstruction of MR brain and tumor structures efficiently under MATLAB platform to recognize the location, volume, and type of brain tumors. In addition, the proposed system applied the Fuzzy C-Means (FCM) algorithm as image segmentation and support vector machine (SVM) as image classification for tumor detection of MR brain images. Results confirmed that this 3D model depicted an advanced view for estimating of human brain diseases.