A CAD system for assessment of MRI findings to track the progression of multiple sclerosis

Abstract

Multiple sclerosis (MS) is a progressive neurological disease affecting myelin pathways. MRI has become the medical imaging study of choice both for the diagnosis and for the follow-up and monitoring of multiple sclerosis. The progression of the disease is variable, and requires routine follow-up to document disease exacerbation, improvement, or stability of the characteristic MS lesions or plaques. The difficulties with using MRI as a monitoring tool are the significant quantities of time needed by the radiologist to actually measure the size of the lesions, and the poor reproducibility of these manual measurements. A CAD system for automatic image analysis improves clinical efficiency and standardizes the lesion measurements. Multiple sclerosis is a disease well suited for automated analysis. The segmentation algorithm devised classifies normal and abnormal brain structures and measures the volume of multiple sclerosis lesions using fuzzy c-means clustering with incorporated spatial (sFCM) information. First, an intracranial structures mask in T1 image data is localized and then superimposed in FLAIR image data. Next, MS lesions are identified by sFCM and quantified within a predefined volume. The initial validation process confirms a satisfactory comparison of automatic segmentation to manual outline by a neuroradiologist and the results will be presented.