Reflection Of MRI Brain Tumor Image Retrieval

964 Words 4 Pages
In this paper, we have developed a shape feature extraction of MRI brain tumor image retrieval. We used T1 weighted image of MRI brain tumor images. There are two modules: feature extraction process and classification. First, the shape features are extracted using techniques like Scale invariant feature transform (SIFT), Harris corner detection and Zernike moments. Second, the supervised learning algorithms like Deep neural network (DNN) and Extreme learning machine (ELM) are used to classify the brain tumor images. Experiments are performed using 1000 brain tumor images. In the performance evaluation, sensitivity, specificity, accuracy, error rate and f-measure are five measures are used. The Experiment result shows that average accuracy has got at Zernike moment– 99%. So Zernike moment is better than SIFT and Harris corner detection techniques. The average time has got at DNN- 0.0901 sec, ELM- 0.0218 sec. So ELM classifier is better than DNN.
Keywords: Feature extraction, Shape features, Brain Tumor and Classifier
1. Introduction
Now days, a keyword search
…show more content…
The diagnosis of brain tumor plays an important role in image processing. Magnetic resonance imaging (MRI) is suited for monitoring and evaluating brain tumors. Coronal, sagittal and axial are the three types of image orientation in brain tumors. The coronal are dividing the body into front and back halves. The sagittal is dividing the body into left and right halves and the axial are dividing the body into upper and lower halves. There are modern techniques used in Digital radiography (X-ray), ultrasound, microscopic imaging (MI), Computed tomography (CT), Magnetic resonance imaging (MRI), Single photon emission computed tomography (SPECT) and Positron emission tomography (PET). MRI is used in the brain for the purpose of bleeding, aneurysms, tumors and

Related Documents