TY - GEN
T1 - Automatic tumor lesion detection and segmentation using modified winnow algorithm
AU - Nabizadeh, N.
AU - Dorodch, M.
AU - Kubat, M.
N1 - Publisher Copyright:
© 2015 IEEE.
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 - SBGFR level set
KW - Tumor lesion detection/segmentation
KW - anisotropic complex Morlet transform
KW - dual tree complex wavelet transform
KW - magnetic resonance imaging
KW - regularized Winnow algorithm
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
AN - SCOPUS:84944316330
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 71
EP - 74
BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PB - IEEE Computer Society
T2 - 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Y2 - 16 April 2015 through 19 April 2015
ER -