Brain Tumour Diagnosis Using Matlab with Edge Detection
Authors: Gaonkar Omkar Maruti , Jadhav Nitesh Sandip , Koli Dishant Krishna , Bhosale Prof. Vijay 
Published: May 2022

Abstract: The segmenting brain tumours in magnetic resonance images (MRI) is a difficult task due to the variety of possible curves, spots, and image concentrations. Brain tumour segmentation is one of the most critical and challenging projects in the field of medical image processing because human-assisted manual characterization can result in inaccurate prediction and diagnosis. [1] A brain tumour is an unusual mass of tissue in which some cells multiply and grow uncontrollably. Furthermore, it is a difficult task when there is an enormous amount of information to be processed. Because brain tumours have a wide range of manifestations and coexist with normal tissues, extracting tumour regions from images becomes complicated. [2] Medical image processing provides basic information of abnormality of brain and it helps the doctor for best treatment planning. This paper specifically aims to detect and localisation tumour regions in the brain using the proposed methodology and patient MRI images.[3] We can derive detailed anatomical information from these high-resolution images in order to examine human brain development and detect abnormalities. Pre-processing, edge detection, and segmentation are the three stages of the proposed methodology. [4] Several tests are performed on the patient to detect cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the most commonly used tests for locating brain tumours. The pre-processing stage involves the conversion of the original image to grayscale and removing any noise that has crept in. [5]The primary step in removing noise and smoothing an MRI image is pre-processing. Following that, segmentation is used to actually indicate the tumor-affected region in the MRI images. Finally, the watershed algorithm is being used to cluster the image. For the implementation of this system, we used MATLAB. Magnetic Resonance Imaging (MRI) has increased in popularity as a high-quality medical imaging technique. [6] The experimental results demonstrated that the proposed approach outperformed existing available approaches in terms of accuracy while maintaining the pathology experts' acceptable accuracy rate. Magnetic resonance imaging (MRI) is a specialized diagnostic imaging technique that provides comprehensive information about human soft tissue anatomy. This methodology allows for extensive clinical practice in the detection of brain tumours, making it simple to identify patients predicated on MR image data. In this paper, we propose a MATLAB programming technique for separating tumour images from brain magnetic resonance (MR) data.[7] The goal of segmentation is to simplify and/or change an image's representation into something more meaningful and easier to analyse. The accuracy of tumour detection is highly noticeable in the MRI image data, and the tumour is clearly highlighted using the proposed MATLAB Coding. These codes are used to enhance the MR image quality by trying to adjust the grey level and applying additional special filters. The MRI dataset confirms that the algorithm's outcomes are more applicable to ordinary output images to identify brain tumours.