  Glomerulus detection is a key step in histopathological evaluation of
microscopy images of kidneys. However, the task of automatic detection of
glomeruli poses challenges due to the disparity in sizes and shapes of
glomeruli in renal sections. Moreover, extensive variations of their
intensities due to heterogeneity in immunohistochemistry staining are also
encountered. Despite being widely recognized as a powerful descriptor for
general object detection, the rectangular histogram of oriented gradients
(Rectangular HOG) suffers from many false positives due to the aforementioned
difficulties in the context of glomerulus detection.
  A new descriptor referred to as Segmental HOG is developed to perform a
comprehensive detection of hundreds of glomeruli in images of whole kidney
sections. The new descriptor possesses flexible blocks that can be adaptively
fitted to input images to acquire robustness to deformations of glomeruli.
Moreover, the novel segmentation technique employed herewith generates high
quality segmentation outputs and the algorithm is assured to converge to an
optimal solution. Consequently, experiments using real world image data reveal
that Segmental HOG achieves significant improvements in detection performance
compared to Rectangular HOG.
  The proposed descriptor and method for glomeruli detection present promising
results and is expected to be useful in pathological evaluation.
