A Contemporary Framework and Novel Method for Segmentation of Brain MRI

Abstract

Brain magnetic resonance imaging plays a vital role in medical image processing for detection of brain tumor, therapy response evaluation, brain tumor diagnosis, and treatment selection. Contrast-enhanced T1C 3D brain magnetic resonance imaging is extensively used brain-imaging modalities. Automated segmentation and detection of brain tumors in contrast-enhanced T1C 3D brain magnetic resonance imaging is a very complicated task due to high disparity in shape, size, and appearance of brain tumor. Nevertheless, so many analyses have been conducted in the similar research work, it remains a very complicated task for automated segmentation and detection of brain tumor in magnetic resonance imaging, and enhancing segmentation accuracy of brain tumor is still continuing field. The main aim of this research work is to develop a fully automated segmentation framework for segmentation and detection of the brain tumor that is allied with contrast-enhanced T1C 3D brain magnetic resonance imaging. Consequently, this research work deals about development of segmentation framework for detection of tumor in brain 3D MR images. The proposed segmentation framework ingrates the most established fuzzy C means clustering method and improved Expectation Maximization (EM) method. An anisotropic filter is used for preprocessing and subsequently, it is employed to the fuzzy C means clustering method, improved Expectation Maximization (EM), and proposed method for superior segmentation and detection of tumor. The performance result of proposed framework is evaluated on 10 patients’ simulated medical clinical dataset. The performance results of the proposed framework exhibited better results as compared with presented methods.