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 Citations (Scopus)

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 publicationIEEE International Conference on Neural Networks - Conference Proceedings
Pages1397-1400
Number of pages4
DOIs
StatePublished - 2007
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: Aug 12 2007Aug 17 2007

Other

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

Fingerprint

Image segmentation
Brain
Annealing
Tissue
Learning systems
Animals
Image processing
Productivity

ASJC Scopus subject areas

  • Software

Cite this

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 IEEE International Conference on Neural Networks - Conference Proceedings (pp. 1397-1400). [4371162] https://doi.org/10.1109/IJCNN.2007.4371162

Automatic brain image segmentation for evaluation of experimental ischemic stroke using gradient vector flow and kernel annealing. / Ozertem, Umut; Gruber, Andras; Erdogmus, Deniz.

IEEE International Conference on Neural Networks - Conference Proceedings. 2007. p. 1397-1400 4371162.

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

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 IEEE International Conference on Neural Networks - Conference Proceedings., 4371162, pp. 1397-1400, 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, United States, 8/12/07. https://doi.org/10.1109/IJCNN.2007.4371162
Ozertem U, Gruber A, Erdogmus D. Automatic brain image segmentation for evaluation of experimental ischemic stroke using gradient vector flow and kernel annealing. In IEEE International Conference on Neural Networks - Conference Proceedings. 2007. p. 1397-1400. 4371162 https://doi.org/10.1109/IJCNN.2007.4371162
Ozertem, Umut ; Gruber, Andras ; Erdogmus, Deniz. / Automatic brain image segmentation for evaluation of experimental ischemic stroke using gradient vector flow and kernel annealing. IEEE International Conference on Neural Networks - Conference Proceedings. 2007. pp. 1397-1400
@inproceedings{e3d1c2cc4ce345889fa781b902bcb438,
title = "Automatic brain image segmentation for evaluation of experimental ischemic stroke using gradient vector flow and kernel annealing",
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.",
author = "Umut Ozertem and Andras Gruber and Deniz Erdogmus",
year = "2007",
doi = "10.1109/IJCNN.2007.4371162",
language = "English (US)",
isbn = "142441380X",
pages = "1397--1400",
booktitle = "IEEE International Conference on Neural Networks - Conference Proceedings",

}

TY - GEN

T1 - Automatic brain image segmentation for evaluation of experimental ischemic stroke using gradient vector flow and kernel annealing

AU - Ozertem, Umut

AU - Gruber, Andras

AU - Erdogmus, Deniz

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=51749090639&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=51749090639&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2007.4371162

DO - 10.1109/IJCNN.2007.4371162

M3 - Conference contribution

SN - 142441380X

SN - 9781424413805

SP - 1397

EP - 1400

BT - IEEE International Conference on Neural Networks - Conference Proceedings

ER -