A probabilistic framework for the detection and tracking in time of multiple sclerosis lesions

Allon Shahar, Hayit Greenspan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

A novel statistical scheme for the automatic detection and tracking in time of relapsing-remitting multiple sclerosis (MS) lesions in image sequences is described. Coherent space-time regions in a four-dimensional feature space (intensity, position (x,y), and time) are extracted by unsupervised clustering using Gaussian mixture modeling. The segments in the sequence pertaining to lesions are automatically detected by context-based classification mechanisms. Lesion segmentation and tracking are performed in a unified manner and not separately, as in other works. A model adaptation stage, in which space-time regions are merged, is introduced for the improvement of lesions' delineation. Qualitative and quantitative results for a sequence of 24 images are shown. The framework's results were validated by comparison to an expert's manual delineation.

Original languageEnglish
Title of host publication2004 2nd IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationMacro to Nano
Pages440-443
Number of pages4
StatePublished - 2004
Externally publishedYes
Event2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano - Arlington, VA, United States
Duration: 15 Apr 200418 Apr 2004

Publication series

Name2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
Volume1

Conference

Conference2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
Country/TerritoryUnited States
CityArlington, VA
Period15/04/0418/04/04

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