  CBCT images suffer from acute shading artifacts primarily due to scatter.
Numerous image-domain correction algorithms have been proposed in the
literature that use patient-specific planning CT images to estimate shading
contributions in CBCT images. However, in the context of radiosurgery
applications such as gamma knife, planning images are often acquired through
MRI which impedes the use of polynomial fitting approaches for shading
correction. We present a new shading correction approach that is independent of
planning CT images. Our algorithm is based on the assumption that true CBCT
images follow a uniform volumetric intensity distribution per material, and
scatter perturbs this uniform texture by contributing cupping and shading
artifacts in the image domain. The framework is a combination of fuzzy C-means
coupled with a neighborhood regularization term and Otsu's method. Experimental
results on artificially simulated craniofacial CBCT images are provided to
demonstrate the effectiveness of our algorithm. Spatial non-uniformity is
reduced from 16% to 7% in soft tissue and from 44% to 8% in bone regions. With
shading-correction, thresholding based segmentation accuracy for bone pixels is
improved from 85% to 91% when compared to thresholding without
shading-correction. The proposed algorithm is thus practical and qualifies as a
plug and play extension into any CBCT reconstruction software for shading
correction.
