Segmentation of sub-cortical structures by the graph-shifts algorithm

Jason J. Corso, Zhuowen Tu, Alan Yuille, Arthur Toga

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

18 Scopus citations

Abstract

We propose a novel algorithm called graph-shifts for performing image segmentation and labeling. This algorithm makes use of a dynamic hierarchical representation of the image. This representation allows each iteration of the algorithm to make both small and large changes in the segmentation, similar to PDE and split-and-merge methods, respectively. In particular, at each iteration we are able to rapidly compute and select the optimal change to be performed. We apply graph-shifts to the task of segmenting sub-cortical brain structures. First we formalize this task as energy function minimization where the energy terms are learned from a training set of labeled images. Then we apply the graphshifts algorithm. We show that the labeling results are comparable in quantitative accuracy to other approaches but are obtained considerably faster: by orders of magnitude (roughly one minute). We also quantitatively demonstrate robustness to initialization and avoidance of local minima in which conventional boundary PDE methods fall.

Original languageEnglish
Title of host publicationInformation Processing in Medical lmaging - 20th International Conference, IPMI 2007, Proceedings
Pages183-197
Number of pages15
DOIs
StatePublished - 2007
Event20th International Conference on Information Processing in Medical lmaging, IPMI 2007 - Kerkrade, Netherlands
Duration: 2 Jul 20076 Jul 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Information Processing in Medical lmaging, IPMI 2007
Country/TerritoryNetherlands
CityKerkrade
Period2/07/076/07/07

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