ABSTRACT
Multiple sclerosis (MS) is also known as the disseminated sclerosis or encephalomyelitis disseminate. It is a demyelinating disease that degrades the protecting covers of nerve cells in the spinal cord and brain. MRI is used to capture correct information of the disease diagnosis. MRI is used to show MS lesions throughout the central nervous system (CNS) and for diagnostic purposes to solve the problem of complexity in a patient's examination. To indicate diagnostic weight of findings and their incorporations into the clinical analysis, MRI classes of evidence for MS are used. Texture-based segmentation techniques like AM-FM (semiautomatic) and saliency maps (automatic) applied on the real patient images give excellent classification results when the extracted feature file (the.do file) is simulated in the VHDL environment. The results show that the accuracy is 94% and is best for the initial week of the disease diagnosis of the patient, which helps in predicting the disease in its earlier stages.
