K-MEANS CLUSTERING OF SPINAL CORD MRI ABNORMALITY FEATURE EXTRACTION

Abstract

Research on Medical Image presents an efficient platform for automated analysis and identification of any deformations in a given medical image data collection, particularly in the Spinal Cord, for more effective and better comprehension of the diagnosis. Tumors, disc hernias, fractures, edoema, and other abnormalities of the spinal cord may be discovered using a variety of medical imaging modalities, including MRI, CT, and fMRI. Fast and reliable analysis of the MRI imaging of the spinal cord using an Automated Decision Support System (ADS) is shown in this study. There are two stages to this process: preparation and execution. Using histograms, the first step is to determine whether any abnormality characteristics or distortions are present in the provided picture. This is the second phase, in which the MRI picture is clustered to determine the depth at which the calcification is present. The algorithm's performance and the amount of time it takes to finish each cluster phase are examined. To further demonstrate its complete accuracy, the algorithm's efficiency is being monitored.