Magnetic Resonance Imaging is considered as a powerful tool for no-invasive diagnosis and description of brain pathologies. This is particularly the case of Multiple Sclerosis, for monitoring this disease and its treatment. Multiple sclerosis is an autoimmune inflammatory disease of the central nervous system which clinical markers are used today for diagnosis and for therapeutic evaluation. In order to automate a long and hard process for the clinician, we propose a semi-automatic segmentation approach of multi sclerosis lesions in longitudinal MRI sequences. We use firstly a robust algorithm that allows spatiotemporal extraction of these lesions by geodesic active contour model. Then, we recommend an original scheme based on an automated image registration technique for evaluating the evolution of the detected lesions. A quantitative study is presented in this paper to validate our results using the BrainWeb simulator. Very promising results are obtained in the case of clinical data.
[1]
박일한,et al.
전자장시스템에서 Level Set Method
,
2008
.
[2]
Xavier Lladó,et al.
Automated detection of multiple sclerosis lesions in serial brain MRI
,
2012,
Neuroradiology.
[3]
Stephen L Hauser,et al.
Diagnosing multiple sclerosis: art and science
,
2017,
The Lancet Neurology.
[4]
Guido Gerig,et al.
Spatio-temporal Segmentation of Active Multiple Sclerosis Lesions in Serial MRI Data
,
2001,
IPMI.
[5]
R Kikinis,et al.
Serial neuropsychological assessment and magnetic resonance imaging analysis in multiple sclerosis.
,
1997,
Archives of neurology.
[6]
David H. Miller,et al.
Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria
,
2017,
The Lancet Neurology.
[7]
F Barkhof,et al.
Role of MR imaging in the diagnosis of MS
,
1996
.