TY - JOUR
T1 - A probabilistic framework for the spatio-temporal segmentation of multiple sclerosis lesions in MR images of the brain
AU - Greenspan, Hayit
AU - Mayer, Arnaldo
AU - Shahar, Allon
PY - 2003
Y1 - 2003
N2 - In this paper we describe the application of a novel statistical image-sequence (video) modeling scheme to sequences of multiple sclerosis (MS) images taken over time. A unique key feature of the proposed framework is the analysis of the image-sequence input as a single entity as opposed to a sequence of separate frames. The extracted space-time regions allow for the detection and identification of disease events and processes, such as the appearance and progression of lesions. According to the proposed methodology, coherent space-time regions in the feature space, and corresponding coherent segments in the video content are extracted by unsupervised clustering via Gaussian mixture modeling (GMM). The parameters of the GMM are determined via the maximum likelihood principle and the Expectation-Maximization (EM) algorithm. The clustering of the image sequence yields a collection of regions (blobs) in a four-dimensional feature space (including intensity, position (x,y), and time). Regions corresponding to MS lesions are automatically identified based on criteria regarding the mean intensity and the size variability over time. The proposed methodology was applied to a registered sequence of 24 T2-weighted MR images acquired from an MS patient over a period of approximately a year. Examples of preliminary qualitative results are shown.
AB - In this paper we describe the application of a novel statistical image-sequence (video) modeling scheme to sequences of multiple sclerosis (MS) images taken over time. A unique key feature of the proposed framework is the analysis of the image-sequence input as a single entity as opposed to a sequence of separate frames. The extracted space-time regions allow for the detection and identification of disease events and processes, such as the appearance and progression of lesions. According to the proposed methodology, coherent space-time regions in the feature space, and corresponding coherent segments in the video content are extracted by unsupervised clustering via Gaussian mixture modeling (GMM). The parameters of the GMM are determined via the maximum likelihood principle and the Expectation-Maximization (EM) algorithm. The clustering of the image sequence yields a collection of regions (blobs) in a four-dimensional feature space (including intensity, position (x,y), and time). Regions corresponding to MS lesions are automatically identified based on criteria regarding the mean intensity and the size variability over time. The proposed methodology was applied to a registered sequence of 24 T2-weighted MR images acquired from an MS patient over a period of approximately a year. Examples of preliminary qualitative results are shown.
KW - Detection
KW - Image sequence modeling
KW - Lesions
KW - Multiple sclerosis
KW - Spatio-temporal segmentation
KW - Tracking
UR - http://www.scopus.com/inward/record.url?scp=0042922569&partnerID=8YFLogxK
U2 - 10.1117/12.481364
DO - 10.1117/12.481364
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AN - SCOPUS:0042922569
SN - 0277-786X
VL - 5032 III
SP - 1551
EP - 1559
JO - Proceedings of SPIE - The International Society for Optical Engineering
JF - Proceedings of SPIE - The International Society for Optical Engineering
T2 - Medical Imaging 2003: Image Processing
Y2 - 17 February 2003 through 20 February 2003
ER -