title: Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data.
abstract: This paper presents a new method for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets. The technique was inspired by procedures developed for analysis of functional MRI data sets. After precise registration of serial volume data sets to 4-D data, we applied a time series analysis taking into account the characteristic time function of variable lesions. The images were preprocessed with a correction of image field inhomogeneities and a normalization of the brightness over the whole time series. Thus, static regions remain unchanged over time, whereas changes in tissue characteristics produce typical intensity variations in the voxel's time series. A set of features was derived from the time series, expressing probabilities for membership to the sought structures. These multiple sources of uncertain evidence were combined to a single evidence value using Dempster-Shafer's theory. The project was driven by the objective of improving the segmentation and characterization of white matter lesions in serial MR data of multiple sclerosis patients. Pharmaceutical research and patient follow-up requires efficient and robust methods with a high degree of automation. The new approach replaces conventional segmentation of series of 3-D data sets by a 1-D processing of the temporal change at each voxel in the 4-D image data set. The new method has been applied to a total of 11 time series from different patient studies, covering time resolutions of 12 and 24 data sets over a period of about 1 year. The results demonstrate that time evolution is a highly sensitive feature for detection of fluctuating structures.
authors.affiliation: University of North Carolina, Department of Computer Science, Chapel Hill, USA. gerig@cs.unc.edu
authors.forename: G
authors.initials: G
authors.lastname: Gerig
authors.forename: D
authors.initials: D
authors.lastname: Welti
authors.forename: C R
authors.initials: CR
authors.lastname: Guttmann
authors.forename: A C
authors.initials: AC
authors.lastname: Colchester
authors.forename: G
authors.initials: G
authors.lastname: Székely
mesh_terms: D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D013997:Time Factors