Abstract
Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure owing to the variability and complexity of the location, size, shape, and texture of these lesions. Because of intensity similarities between brain lesions and normal tissues, some approaches make use of multi-spectral anatomical MRI scans. However, the time and cost restrictions for collecting multi-spectral MRI scans and some other difficulties necessitate developing an approach that can detect tumor tissues using a single-spectral anatomical MRI images. In this paper, we present a fully automatic system, which is able to detect slices that include tumor and, to delineate the tumor area. The experimental results on single contrast mechanism demonstrate the efficacy of our proposed technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity. Moreover, we include a study evaluating the efficacy of statistical features over Gabor wavelet features using several classifiers. This contribution fills the gap in the literature, as is the first to compare these sets of features for tumor segmentation applications.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 286-301 |
| Number of pages | 16 |
| Journal | Computers and Electrical Engineering |
| Volume | 45 |
| DOIs | |
| State | Published - Jul 1 2015 |
Fingerprint
Keywords
- Fluid-attenuated inversion recovery
- Gabor wavelet features
- Lesion detection/segmentation
- MR imaging
- Statistical features
- T1-weighted
ASJC Scopus subject areas
- Computer Science(all)
- Electrical and Electronic Engineering
- Control and Systems Engineering
Cite this
Brain tumors detection and segmentation in MR images : Gabor wavelet vs. statistical features. / Nabizadeh, Nooshin; Kubat, Miroslav.
In: Computers and Electrical Engineering, Vol. 45, 01.07.2015, p. 286-301.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Brain tumors detection and segmentation in MR images
T2 - Gabor wavelet vs. statistical features
AU - Nabizadeh, Nooshin
AU - Kubat, Miroslav
PY - 2015/7/1
Y1 - 2015/7/1
N2 - Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure owing to the variability and complexity of the location, size, shape, and texture of these lesions. Because of intensity similarities between brain lesions and normal tissues, some approaches make use of multi-spectral anatomical MRI scans. However, the time and cost restrictions for collecting multi-spectral MRI scans and some other difficulties necessitate developing an approach that can detect tumor tissues using a single-spectral anatomical MRI images. In this paper, we present a fully automatic system, which is able to detect slices that include tumor and, to delineate the tumor area. The experimental results on single contrast mechanism demonstrate the efficacy of our proposed technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity. Moreover, we include a study evaluating the efficacy of statistical features over Gabor wavelet features using several classifiers. This contribution fills the gap in the literature, as is the first to compare these sets of features for tumor segmentation applications.
AB - Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure owing to the variability and complexity of the location, size, shape, and texture of these lesions. Because of intensity similarities between brain lesions and normal tissues, some approaches make use of multi-spectral anatomical MRI scans. However, the time and cost restrictions for collecting multi-spectral MRI scans and some other difficulties necessitate developing an approach that can detect tumor tissues using a single-spectral anatomical MRI images. In this paper, we present a fully automatic system, which is able to detect slices that include tumor and, to delineate the tumor area. The experimental results on single contrast mechanism demonstrate the efficacy of our proposed technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity. Moreover, we include a study evaluating the efficacy of statistical features over Gabor wavelet features using several classifiers. This contribution fills the gap in the literature, as is the first to compare these sets of features for tumor segmentation applications.
KW - Fluid-attenuated inversion recovery
KW - Gabor wavelet features
KW - Lesion detection/segmentation
KW - MR imaging
KW - Statistical features
KW - T1-weighted
UR - http://www.scopus.com/inward/record.url?scp=84942368836&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84942368836&partnerID=8YFLogxK
U2 - 10.1016/j.compeleceng.2015.02.007
DO - 10.1016/j.compeleceng.2015.02.007
M3 - Article
AN - SCOPUS:84942368836
VL - 45
SP - 286
EP - 301
JO - Computers and Electrical Engineering
JF - Computers and Electrical Engineering
SN - 0045-7906
ER -