Abstract
Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure due to the variability and complexity of the location, size, shape, and texture of these lesions. Due to intensity similarities between brain lesions and normal tissues, most approaches make use of multi-spectral MRI images. However, the time, cost, and data process restrictions for collecting multi-spectral MRI necessitate developing a lesion detection and segmentation approach that can detect lesions using a single anatomical MRI image. In this paper, we present a fully automatic system, which is able to detect the MRI images that include tumor and to segment the tumor area. Fully anisotropic complex Morlet transform, and dual tree complex wavelet transform are introduced for tumor textural characterization. Perhaps most importantly, we propose a novel feature selection technique that is based on regularized Winnow algorithm. An active contour model implemented with selective binary and Gaussian filtering regularized level set (SBGFRLS) is used for final segmentation step. The experimental results on both simulated and real brain MRI data prove the efficacy of our technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - International Symposium on Biomedical Imaging |
| Publisher | IEEE Computer Society |
| Pages | 71-74 |
| Number of pages | 4 |
| Volume | 2015-July |
| ISBN (Print) | 9781479923748 |
| DOIs | |
| State | Published - Jul 21 2015 |
| Event | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: Apr 16 2015 → Apr 19 2015 |
Other
| Other | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 |
|---|---|
| Country | United States |
| City | Brooklyn |
| Period | 4/16/15 → 4/19/15 |
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Keywords
- anisotropic complex Morlet transform
- dual tree complex wavelet transform
- magnetic resonance imaging
- regularized Winnow algorithm
- SBGFR level set
- Tumor lesion detection/segmentation
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
Automatic tumor lesion detection and segmentation using modified winnow algorithm. / Nabizadeh, N.; Dorodch, M.; Kubat, Miroslav.
Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July IEEE Computer Society, 2015. p. 71-74 7163819.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automatic tumor lesion detection and segmentation using modified winnow algorithm
AU - Nabizadeh, N.
AU - Dorodch, M.
AU - Kubat, Miroslav
PY - 2015/7/21
Y1 - 2015/7/21
N2 - Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure due to the variability and complexity of the location, size, shape, and texture of these lesions. Due to intensity similarities between brain lesions and normal tissues, most approaches make use of multi-spectral MRI images. However, the time, cost, and data process restrictions for collecting multi-spectral MRI necessitate developing a lesion detection and segmentation approach that can detect lesions using a single anatomical MRI image. In this paper, we present a fully automatic system, which is able to detect the MRI images that include tumor and to segment the tumor area. Fully anisotropic complex Morlet transform, and dual tree complex wavelet transform are introduced for tumor textural characterization. Perhaps most importantly, we propose a novel feature selection technique that is based on regularized Winnow algorithm. An active contour model implemented with selective binary and Gaussian filtering regularized level set (SBGFRLS) is used for final segmentation step. The experimental results on both simulated and real brain MRI data prove the efficacy of our technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity.
AB - Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure due to the variability and complexity of the location, size, shape, and texture of these lesions. Due to intensity similarities between brain lesions and normal tissues, most approaches make use of multi-spectral MRI images. However, the time, cost, and data process restrictions for collecting multi-spectral MRI necessitate developing a lesion detection and segmentation approach that can detect lesions using a single anatomical MRI image. In this paper, we present a fully automatic system, which is able to detect the MRI images that include tumor and to segment the tumor area. Fully anisotropic complex Morlet transform, and dual tree complex wavelet transform are introduced for tumor textural characterization. Perhaps most importantly, we propose a novel feature selection technique that is based on regularized Winnow algorithm. An active contour model implemented with selective binary and Gaussian filtering regularized level set (SBGFRLS) is used for final segmentation step. The experimental results on both simulated and real brain MRI data prove the efficacy of our technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity.
KW - anisotropic complex Morlet transform
KW - dual tree complex wavelet transform
KW - magnetic resonance imaging
KW - regularized Winnow algorithm
KW - SBGFR level set
KW - Tumor lesion detection/segmentation
UR - http://www.scopus.com/inward/record.url?scp=84944316330&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944316330&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2015.7163819
DO - 10.1109/ISBI.2015.7163819
M3 - Conference contribution
SN - 9781479923748
VL - 2015-July
SP - 71
EP - 74
BT - Proceedings - International Symposium on Biomedical Imaging
PB - IEEE Computer Society
ER -