title: Multiple sclerosis lesion quantification using fuzzy-connectedness principles.
abstract: Multiple sclerosis (MS) is a disease of the white matter. Magnetic resonance imaging (MRI) is proven to be a sensitive method of monitoring the progression of this disease and of its changes due to treatment protocols. Quantification of the severity of the disease through estimation of MS lesion volume via MR imaging is vital for understanding and monitoring the disease and its treatment. This paper presents a novel methodology and a system that can be routinely used for segmenting and estimating the volume of MS lesions via dual-echo fast spin-echo MR imagery. A recently developed concept of fuzzy objects forms the basis of this methodology. An operator indicates a few points in the images by pointing to the white matter, the grey matter, and the cerebro-spinal fluid (CSF). Each of these objects is then detected as a fuzzy connected set. The holes in the union of these objects correspond to potential lesion sites which are utilized to detect each potential lesion as a three-dimensional (3-D) fuzzy connected object. These objects are presented to the operator who indicates acceptance/rejection through the click of a mouse button. The number and volume of accepted lesions is then computed and output. Based on several evaluation studies, we conclude that the methodology is highly reliable and consistent, with a coefficient of variation (due to subjective operator actions) of 0.9% (based on 20 patient studies, three operators, and two trials) for volume and a mean false-negative volume fraction of 1.3%, with a 95% confidence interval of 0%-2.8% (based on ten patient studies).
authors.affiliation: Department of Radiology, University of Pennsylvania, Philadelphia 19104-6021, USA. jay@mipg.upenn.edu
authors.forename: J K
authors.initials: JK
authors.lastname: Udupa
authors.forename: L
authors.initials: L
authors.lastname: Wei
authors.forename: S
authors.initials: S
authors.lastname: Samarasekera
authors.forename: Y
authors.initials: Y
authors.lastname: Miki
authors.forename: M A
authors.initials: MA
authors.lastname: van Buchem
authors.forename: R I
authors.initials: RI
authors.lastname: Grossman
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D002985:Clinical Protocols; D016287:Computer Peripherals; D016001:Confidence Intervals; D003287:Contrast Media; D003628:Database Management Systems; D018450:Disease Progression; D005069:Evaluation Studies as Topic; D005188:False Negative Reactions; D017143:Fuzzy Logic; D019786:Gadolinium DTPA; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000134:cerebrospinal fluid / Q000175:diagnosis / Q000473:pathology* / Q000503:physiopathology / Q000628:therapy; D015588:Observer Variation; D015203:Reproducibility of Results
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA