Brain tumor classification from multi-modality MRI using wavelets and machine learning
In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans. The data from multi-modal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skull-stripped, and the histogram matching is...
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| Published in | Pattern analysis and applications : PAA Vol. 20; no. 3; pp. 871 - 881 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.08.2017
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans. The data from multi-modal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skull-stripped, and the histogram matching is performed with a reference volume of high contrast. From the preprocessed images, the following features are then extracted: intensity, intensity differences, local neighborhood and wavelet texture. The integrated features are subsequently provided to the
random forest
classifier to predict five classes: background, necrosis, edema, enhancing tumor and non-enhancing tumor, and then these class labels are used to hierarchically compute three different regions (
complete tumor, active tumor and enhancing tumor
). We performed a leave-one-out cross-validation and achieved 88%
Dice overlap
for the complete tumor region, 75% for the core tumor region and 95% for enhancing tumor region, which is higher than the Dice overlap reported from MICCAI BraTS challenge. |
|---|---|
| AbstractList | In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans. The data from multi-modal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skull-stripped, and the histogram matching is performed with a reference volume of high contrast. From the preprocessed images, the following features are then extracted: intensity, intensity differences, local neighborhood and wavelet texture. The integrated features are subsequently provided to the random forest classifier to predict five classes: background, necrosis, edema, enhancing tumor and non-enhancing tumor, and then these class labels are used to hierarchically compute three different regions (complete tumor, active tumor and enhancing tumor). We performed a leave-one-out cross-validation and achieved 88% Dice overlap for the complete tumor region, 75% for the core tumor region and 95% for enhancing tumor region, which is higher than the Dice overlap reported from MICCAI BraTS challenge. In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans. The data from multi-modal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skull-stripped, and the histogram matching is performed with a reference volume of high contrast. From the preprocessed images, the following features are then extracted: intensity, intensity differences, local neighborhood and wavelet texture. The integrated features are subsequently provided to the random forest classifier to predict five classes: background, necrosis, edema, enhancing tumor and non-enhancing tumor, and then these class labels are used to hierarchically compute three different regions ( complete tumor, active tumor and enhancing tumor ). We performed a leave-one-out cross-validation and achieved 88% Dice overlap for the complete tumor region, 75% for the core tumor region and 95% for enhancing tumor region, which is higher than the Dice overlap reported from MICCAI BraTS challenge. |
| Author | Usman, Khalid Rajpoot, Kashif |
| Author_xml | – sequence: 1 givenname: Khalid surname: Usman fullname: Usman, Khalid email: 12mscskusman@seecs.edu.pk organization: School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST) – sequence: 2 givenname: Kashif surname: Rajpoot fullname: Rajpoot, Kashif organization: School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), School of Computer Science, University of Birmingham |
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| SubjectTerms | Brain Brain cancer Classification Computer Science Edema Feature extraction Image contrast Image segmentation Machine learning Magnetic resonance imaging NMR Nuclear magnetic resonance Pattern Recognition Short Paper Skull Wavelet analysis |
| Title | Brain tumor classification from multi-modality MRI using wavelets and machine learning |
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