Probabilistic spatial-temporal segmentation of multiple sclerosis lesions

Allon Shahar*, Hayit Greenspan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this paper we describe the application of a novel statistical video-modeling scheme to sequences of multiple sclerosis (MS) images taken over time. The analysis of the image-sequence input as a single entity, as opposed to a sequence of separate frames, is a unique feature of the proposed framework. Coherent space-time regions in a four-dimensional feature space (intensity, position (x,y), and time) and corresponding coherent segments in the video content are extracted by unsupervised clustering via Gaussian mixture modeling (GMM). The Expectation-Maximization (EM) algorithm is used to determine the parameters of the model according to the maximum likelihood principle. MS lesions are automatically detected, segmented and tracked in time by context-based classification mechanisms. Qualitative and quantitative results of the proposed methodology are shown for a sequence of 24 T2-weighted MR images, which was acquired from a relapsing-remitting MS patient over a period of approximately a year. The validation of the framework was performed by a comparison to an expert radiologist's manual delineation.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMilan Sonka, Ioannis A. Kakadiaris, Jan Kybic
PublisherSpringer Verlag
Pages269-280
Number of pages12
ISBN (Print)3540226753, 9783540226758
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3117
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Dive into the research topics of 'Probabilistic spatial-temporal segmentation of multiple sclerosis lesions'. Together they form a unique fingerprint.

Cite this