Abstract
In this paper the automatic and customized brain segmentation followed by a stroke lesion detection technique is presented applying single modality Magnetic Resonance Images (MRIs). A novel intensity-based segmentation technique called gravitational histogram optimization is developed for this purpose. By applying histogram gravitational optimization algorithm the brain can be segmented into discriminative area including stroke lesion. The mathematical descriptions as well as the convergence criteria of the developed algorithm are presented in detail. The application of the proposed algorithm in the segmentation of single Diffusion-Weighted Images (DWI) modality of healthy and lesion MR image slices for different number of segments is presented and the results are discussed. The segmented areas are then employed in automatic lesion slice detection and lesion extraction technique. The stroke lesion is extracted from the recognized lesion slice with acceptable accuracy.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013 |
| Editors | Hamid R. Arabnia, Leonidas Deligiannidis, Joan Lu, Fernando G. Tinetti, Jane You, George Jandieri, Gerald Schaefer, Ashu M. G. Solo, Vladimir Volkov |
| Publisher | CSREA Press |
| Pages | 207-213 |
| Number of pages | 7 |
| ISBN (Electronic) | 1601322526, 9781601322524 |
| State | Published - Jan 1 2013 |
| Event | 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013, at WORLDCOMP 2013 - Las Vegas, United States Duration: Jul 22 2013 → Jul 25 2013 |
Publication series
| Name | Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013 |
|---|---|
| Volume | 1 |
Conference
| Conference | 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013, at WORLDCOMP 2013 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 7/22/13 → 7/25/13 |
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ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
Cite this
Automatic ischemic stroke lesion segmentation using single MR modality and gravitational histogram optimization based brain segmentation. / Nabizadeh, Nooshin; Kubat, Miroslav; John, Nigel; Wright, Clinton.
Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013. ed. / Hamid R. Arabnia; Leonidas Deligiannidis; Joan Lu; Fernando G. Tinetti; Jane You; George Jandieri; Gerald Schaefer; Ashu M. G. Solo; Vladimir Volkov. CSREA Press, 2013. p. 207-213 (Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013; Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automatic ischemic stroke lesion segmentation using single MR modality and gravitational histogram optimization based brain segmentation
AU - Nabizadeh, Nooshin
AU - Kubat, Miroslav
AU - John, Nigel
AU - Wright, Clinton
PY - 2013/1/1
Y1 - 2013/1/1
N2 - In this paper the automatic and customized brain segmentation followed by a stroke lesion detection technique is presented applying single modality Magnetic Resonance Images (MRIs). A novel intensity-based segmentation technique called gravitational histogram optimization is developed for this purpose. By applying histogram gravitational optimization algorithm the brain can be segmented into discriminative area including stroke lesion. The mathematical descriptions as well as the convergence criteria of the developed algorithm are presented in detail. The application of the proposed algorithm in the segmentation of single Diffusion-Weighted Images (DWI) modality of healthy and lesion MR image slices for different number of segments is presented and the results are discussed. The segmented areas are then employed in automatic lesion slice detection and lesion extraction technique. The stroke lesion is extracted from the recognized lesion slice with acceptable accuracy.
AB - In this paper the automatic and customized brain segmentation followed by a stroke lesion detection technique is presented applying single modality Magnetic Resonance Images (MRIs). A novel intensity-based segmentation technique called gravitational histogram optimization is developed for this purpose. By applying histogram gravitational optimization algorithm the brain can be segmented into discriminative area including stroke lesion. The mathematical descriptions as well as the convergence criteria of the developed algorithm are presented in detail. The application of the proposed algorithm in the segmentation of single Diffusion-Weighted Images (DWI) modality of healthy and lesion MR image slices for different number of segments is presented and the results are discussed. The segmented areas are then employed in automatic lesion slice detection and lesion extraction technique. The stroke lesion is extracted from the recognized lesion slice with acceptable accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85000655974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85000655974&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85000655974
T3 - Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013
SP - 207
EP - 213
BT - Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013
A2 - Arabnia, Hamid R.
A2 - Deligiannidis, Leonidas
A2 - Lu, Joan
A2 - Tinetti, Fernando G.
A2 - You, Jane
A2 - Jandieri, George
A2 - Schaefer, Gerald
A2 - Solo, Ashu M. G.
A2 - Volkov, Vladimir
PB - CSREA Press
ER -