Minimum Variance Distortionless Response Beamforming for Tumor Segmentation in MRI 49 Minimum Variance Distortionless Response Beamforming for Tumor Segmentation in MRI

Image classification it generally requires a priori knowledge about the objects to be classified. In this paper, we present a new method to segment tumor in multispectral magnetic resonance (MR) images of the human brain. The proposed approach, called Minimum Variance Distortionless Response beamforming (MVDR) was introduced in [15] where only the knowledge of the desired signature to be classified was required. It was a special case of Linearly Constrained Minimum Variance Beamforming (LCMV) in array processing. MVDR considers an MR image classification problem as an array-processing problem where each sensor represents one spectral band. It uses a finite impulse response (FIR) filter to minimize the output power while the desired signature is constrained to a specific gain. That is the response of the beamformer is constrained to equal unity at the electrical angle. The method has been evaluated through several experiments. Results sh ow that the cerebral tissue was segmented accurately into four images, tumor, gray matter, white matter and cerebral spinal fluid indicating the possible usefulness of this method. As far as computing saving is concerned, the experimental results also show computational complexity improvement.