Artificial Neural Network Based Brain MRI Using DCT

Authors: Rida Khan, Mrs.Nisha Bhati

Certificate: View Certificate

Abstract

The classification in recovery systems Brain is to differentiate among normal and abnormal brain tissue. In this paper use feature extraction from MRI is carried out by Wavelet transform and ANN techniques. Wavelet transform tool for feature extraction because it gives better contrast to an image. Due to better contrast it improves easily hanging signals of an image and reduces the overhead. GLCM is used to select the best features for classification..

Introduction

The brain is the most fascinating and least understood, organ in the human body. For centuries, scientists and philosophers have pondered the relationship between behavior, emotion, memory, thought, consciousness, and the physical body. In the Middle Ages there was much controversy as to whether the soul was located in the brain or in the heart. As ideas developed however, it was suggested that mental processes were located in the ventricles of the brain. According to this theory 'common sense' was located in the lateral ventricles, along with imagination accommodated in the posterior part. The third ventricle was the seat of reasoning, judgment and thought, whilst memory was contained in the fourth ventricle. Medical Image analysis and processing has great significance in the field of medicine, especially in Noninvasive treatment and clinical study. This is medical imaging techniques analysis tools enable both doctors and radiologists to arrive at a specific diagnosis process. Medical Image Processing has emerged as one of the most important tools to identify as well as diagnose various disorders. Imaging helps of the doctors to visualize and analyze the image for understanding of abnormalities in internal structures. In brain images data obtained from Bio-medical Devices which use imaging techniques like Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and mammogram function, which indicates the presence or absence of the lesion along with the patient history, of important factor in the diagnosis process

Conclusion

In this work , we are proposed a medical decisionsystem with two class sets as normal and abnormal. This automatic detection system which is designed by gray-level cooccurrence matrix (GLCM) and supervised learning method (ANN) and Wavelet transform obtain promising results to assist the diagnosis brain disease .The methodology in this paper is based on using image features and employing ANN classifier to distinguish normal and abnormal brain MRI .The accuracy of the system is 75% .