Tumorous Slices Classification from MRI Brain Volumes using Block based Features Extraction and Random Forest Classifier
Keywords:
Tumor detection, Random forest, Feature extraction, Classification, BraTS datasetAbstract
The proposed work presents a fully automatic computer-aided diagnosis (CAD) system for magnetic resonance images (MRI) of brain tumor classification. Tumorous slices classification is one of the preprocess steps for brain tumor segmentation and visualization. The proposed work classifies each scan image of MRI volumes into normal or tumorous using block based feature extraction and random forest (RF) classifier. The given image has divided into 8 × 8 non overlapping blocks and extracted three Haralick features such as Energy, inverse difference moment (IDM) and directional moment (DM) from each block. These three extracted features of training blocks are helped to train the RF classifier. The MRI materials used are gathered from multimodal brain tumor segmentation (BraTS 2015) training dataset comprises 274 multisequence MR scans of glioma patients. The experimental results of proposed technique are validated using the measures sensitivity, specificity, accuracy, missed alarm (MA) and false alarm (FA). The average results of the proposed method reached upto 94% of sensitivity, 94% of specificity and 95% of accuracy in BraTS 2015. The error rates measures 1% of slices were missed to identify as tumor and 3% of slices spuriously detected as tumor. The performance of the proposed work was compared with eight existing methods. In summary, the results showed that the proposed method using RF classifier given effective classification for separating normal and tumorous slices from MR brain volumes
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