Abstract
Researchers in the field of computer vision and medical imaging acknowledge that segmentation trivializes the task of bias field correction, and bias field distorts only the intensity but not the spatial attributes of an image. We exploit this knowledge to develop a new algorithm that corrects bias field in magnetic resonance images of brain. K-means algorithm generates regions of interest (ROIs) equal to the number of anatomic structures in the test image. Anatomic structural map is derived by combining entropy image from the output of multiscale edge detector with output of Otsu threshold. Spatial information from anatomic structural map aids detection of outliers in each ROI. Outliers are assigned to their appropriate tissue classes. The task of Intensity inhomogeneity correction is completed by rescaling intensity levels of voxels in each tissue class to conform to statistics of uncorrupted voxels. Performance evaluation demonstrates efficiency of our proposal for different characteristics of bias field.
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