Magnetic resonance imaging (MRI) is a noninvasive method for producing three-dimensional tomographic images of the human body. MRI is most often used for the detection of tumors, lesions, and other abnormalities in soft tissues, such as the brain.
Several techniques for automatically segmenting brain tissues in MRI scans of the head have recently been developed. One goal of segmentation is to automatically or semi-automatically detect lesions in the brains of multiple sclerosis patients. The number and size of lesions indicate the progression of the disease in the patient. Therefore, automatic lesion detection may significantly aid in the analysis of treatments.
Segmentation is problematic due to radio frequency inhomogeneity (image intensity variation) caused by inaccuracies in the magnetic resonance scanner and by nonuniform loading of the scanner coils by the patient. The segmentation algorithms also have difficulty dealing with tissues outside the brain, such as skin, fat, and bone. Consequently, intensity correction and the removal of non-brain tissues are mandatory for successful automatic segmentation.
A new method for automatic intracranial boundary detection and radio frequency correction in MRI is described in this thesis. The intracranial boundary detection method isolates the brain using nonlinear anisotropic diffusion. It then uses active contour models to find the brain's edge. The radio frequency correction technique employs a fast homomorphic filter to reduce low-frequency intensity variation in voxels within the brain.
The new intracranial boundary detection method proved effective on five MRI data sets from two different MRI scanners. The radio frequency correction technique reduced intensity variation due to radio frequency inhomogeneity in the three MRI data sets on which it was tested.