Automatic brain image segmentation for evaluation of experimental ischemic stroke using gradient vector flow and kernel annealing

Umut Ozertem, Andras Gruber, Deniz Erdogmus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Ischemic stroke is the most prevalent catastrophic disease of the brain. Various animal models have been used to study the disease. The majority of the models are based on induction of focal ischemic cerebral necrosis, followed by exhaustive morphometric analysis of the tissues. Despite recent advances in machine learning and image processing, neurological damage evaluations are still based on tedious manual or semi-automatic segmentation of brain images. We demonstrate a method that uses active contours combined with a kernel annealing approach to automatically segment the brain organs of interest, as well as a simple feature that highlights the contrast between normal and infarct brain tissue for automated analysis. The automated segmentation and analysis solution will be useful for increasing the productivity of experimentation and removing investigator bias from the data analysis.

Original languageEnglish (US)
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages1397-1400
Number of pages4
DOIs
StatePublished - Dec 1 2007
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: Aug 12 2007Aug 17 2007

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

Other2007 International Joint Conference on Neural Networks, IJCNN 2007
CountryUnited States
CityOrlando, FL
Period8/12/078/17/07

ASJC Scopus subject areas

  • Software

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    Ozertem, U., Gruber, A., & Erdogmus, D. (2007). Automatic brain image segmentation for evaluation of experimental ischemic stroke using gradient vector flow and kernel annealing. In The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings (pp. 1397-1400). [4371162] (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/IJCNN.2007.4371162