  The sheer volume and size of histopathological images (e.g.,10^6 MPixel)
underscores the need for faster and more accurate Regions-of-interest (ROI)
detection algorithms. In this paper, we propose such an algorithm, which has
four main components that help achieve greater accuracy and faster speed:
First, while using coarse-to-fine topology preserving segmentation as the
baseline, the proposed algorithm uses a superpixel regularity optimization
scheme for avoiding irregular and extremely small superpixels. Second, the
proposed technique employs a prediction strategy to focus only on important
superpixels at finer image levels. Third, the algorithm reuses the information
gained from the coarsest image level at other finer image levels. Both the
second and the third components drastically lower the complexity. Fourth, the
algorithm employs a highly effective parallelization scheme using adap- tive
data partitioning, which gains high speedup. Experimental results, conducted on
the BSD500 [1] and 500 whole-slide histological images from the National Lung
Screening Trial (NLST)1 dataset, confirm that the proposed algorithm gained 13
times speedup compared with the baseline, and around 160 times compared with
SLIC [11], without losing accuracy.
