A Model for storage optimization of brain MRI images for tumor detection using image processing technique
Magnetic Resonance Imaging (MRI) is a major non-invasive method for Brain tumor detection. The anatomical assessment of brain newlinetumor can be carried out using brain MRI image analysis. MRI is widely used in brain tumor identification and classification. Images generated during the diagnosis purpose are unattended after the specified diagnosis. newlineBrain MRI images in Digital Imaging and Communications in Medicine (DICOM) format require large amount of storage space. newlineAccumulation of the MRI images put forward the requirement of more storage space. To store large number of images, existing storage models has to be handled wisely. Research associated with storage, process and newlinemanipulation of medical image data using modern technologies with a minimal human intervention is the need of the time. Image processing deals with the study and development of innovative technologies for newlineanalysis,representation and interpretation of the image data. In this research, the need of an efficient storage model that can help in storing the brain MRI images is studied with the help of image processing technique. To store the brain MRI images with a reduced storage space, a matrix-based method is proposed. In this model brain MRI images in newlineDICOM format are converted into matrix format. In the DICOM images, the image data and header information together hold the details of the patient and image data. These data are converted and stored in the matrix. newlineThe stored matrix is accessed as the input to the proposed model. The proposed model follows different image processing steps.The process starts with pre-processing of brain MRI images followed by clustering of newlinewhite matter (WM), gray matter (GM) and cerebrospinal fluid (CSF), segmentation of tumor and classification of tumor and finally it handles the storage of the MRI images. In the pre-processing step, filtering algorithms are applied on MRI to remove the noise and text artifacts. The newlinewhite matter, gray matter and CSF are separated using the K-means newlineclustering method.