Efficient Framework for Identifying, Locating, Detecting and Classifying MRI Brain Tumor in MRI Images

Abstract

Image processing has plays vital role in today's technological world. It can be applied in numerous application areas such as medical, remote sensing, computer vision etc. Brain tumor is caused due to formation of abnormal tissues within human brain. Therefore, it is necessary to remove affected tumor part from the brain securely. Among various medical imaging techniques Magnetic Resonance Imaging (MRI) employs a vital role to generate images of internal parts of human body. Image segmentation is one of the challenging tasks in today's medical field. An effective segmentation using MRI slices can help to identifying the tumor with its actual size and shape. To meet this requirement, a novel method called Adaptive Convex Region Contour (ACRC) algorithm is presented. Here, Support Vector Machine (SVM) is utilized for slice classification whether it is normal or abnormal. After obtaining SVM results, abnormal slices are involved in segmentation process. Since, human body is having complicated 3D anatomical structure naturally. Unfortunately, MRI slices are yields only 2Dimensional images. The actual shape of tumor cannot be clearly visualized in 2D form. Hence, transformation from 2D to 3D is essential which helps the doctors during surgery. The Rapid Mode Image Matching (RMIM) algorithm has to be followed for 3D reconstruction modeling. After building 3D model, the original volume of the tumor is estimated. The precise experimentation was implemented in MATLAB simulation environment. The obtained results are confirmed that proposed method has better accurate results compared to existing methods.

Keywords: 3D reconstruction and volume estimation; Adaptive convex region contour (ACRC); Brain tumor; Image classification; MRI slices.