Abstract
When soft tissue planning is important, usually, the Magnetic Resonance Imaging (MRI) is a medical imaging technique of selection. In this work, we show a modern method for automated diagnosis depending on a magnetic resonance images classification of the MRI. The presented technique has two main stages; features extraction and classification. We obtained the features corresponding to MRI images implementing Discrete Wavelet Transformation (DWT), inverse and forward, and textural properties, like rotation invariant texture features based on Gabor filtering, and evaluate the meaning of every property in the classification. The classifier is according to Feed Forward Back Propagation Artificial Neural Network (FP-ANN) in the classification stage. The properties thereafter derived to be implemented to teach a neural network based binary classifier that will be automatically able to conclude whether the image is that of a pathological, suffering from brain lesion, or a normal brain. The proposed algorithm obtained the sensitivity of 97.50%, specificity of 82.86% and accuracy of 94.3% for clinical Brain MRI database. This outcome proofs that the presented algorithm is robust and effective compared with other recent techniques.
Highlights
Magnetic Resonance Imaging is used for in vivo imaging of soft tissue in the body
We show a modern method for automated diagnosis depending on a magnetic resonance images classification of the Magnetic Resonance Imaging (MRI)
We present the data sets of MRI images used in this work the system architecture of the proposed algorithm
Summary
Magnetic Resonance Imaging is used for in vivo imaging of soft tissue in the body. By using magnetic fields together with physical properties of atoms in the body, different tissue types can be distinguished and visualized. In the step, additional classification is done for specifying the tumor into benign or malignant This algorithm specifies whether an input image of MRI brain performs a tumor or health brain as percentage. It identifies the tumor type; benign or malignant tumor [7]. In 2012, Chan and Gal, presented a supervised machine-learning based technique to detect an artery voxels in DCE-MRI of the brain This algorithm uses a group of kinetic and local structural properties with a logistic retraction classifier to identify if the arterial voxels is in the image.
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