Improved identification and quantification of lesions in multiple sclerosis

Abstract

Correct identification of multiple sclerosis (MS) lesions in MRI is important for monitoring disease progression and for assessing treatment effects. We present a framework to automatically detect lesions of MS patients based on affine transformation followed by segmentation, background removal, and morphological filtering. The image processing procedure was tested with ten data sets of MRI images of several stages of multiple sclerosis. Analyses were also performed using the developed algorithm on the images obtained with different data sets. Compared to existing methods, this approach enhances the accuracy and reliability of proposed work.