Brain MRI Images for Tumour Detection Using Storage Optimisation Technique

A brain tumour is caused by a mass of random cells inside the brain, which is dangerous and harmful to the brain. Today, it is difficult to accurately recognise brain images. The current demand is for research on the storage, processing, and manipulation of medical image data utilising contemporary technology with little human involvement. The study and creation of cutting-edge technology for the analysis, representation, and interpretation of visual data are known as image processing. The requirement for an effective storage model that may assist in saving the brain MRI images is investigated in this study with the use of an image processing approach. A matrix-based technique is suggested to store the brain MRI pictures with less storage capacity. This model converts DICOM-formatted brain MRI images into matrix format. The patient’s information and image data are combined in the DICOM images’ image data and header information. These information are transformed and put in the matrix. The suggested model’s input is obtained from the stored matrix. The suggested model employs many stages of image processing. The procedure begins with pre-processing of the brain MRI pictures, then clusters the white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF), segments the tumour, and categorises the tumour before handling storage of the MRI images. Filtering methods are used on MRI data at the pre-processing stage to get rid of noise and text artefacts. The K-means clustering technique is used to separate the white matter, grey matter, and CSF. With regard to both pixels and edges, the MRI picture is segmented. When it comes to tumour classification, RBF kernel-based SVM ensemble performed better, and SVM ensemble is used to classify brain tumours.