  Automated detection of visually salient regions is an active area of research
in computer vision. Salient regions can serve as inputs for object detectors as
well as inputs for region based registration algorithms. In this paper we
consider the problem of speeding up computationally intensive bottom-up salient
region detection in 3D medical volumes.The method uses the Kadir Brady
formulation of saliency. We show that in the vicinity of a salient region,
entropy is a monotonically increasing function of the degree of overlap of a
candidate window with the salient region. This allows us to initialize a sparse
seed-point grid as the set of tentative salient region centers and iteratively
converge to the local entropy maxima, thereby reducing the computation
complexity compared to the Kadir Brady approach of performing this computation
at every point in the image. We propose two different approaches for achieving
this. The first approach involves evaluating entropy in the four quadrants
around the seed point and iteratively moving in the direction that increases
entropy. The second approach we propose makes use of mean shift tracking
framework to affect entropy maximizing moves. Specifically, we propose the use
of uniform pmf as the target distribution to seek high entropy regions. We
demonstrate the use of our algorithm on medical volumes for left ventricle
detection in PET images and tumor localization in brain MR sequences.
