In this work, we have developed a computational pipeline to detect glomeruli from whole-slide images (WSIs) of renal tissue sections marked with histological stains. Multi-radial color local binary pattern (mrcLBP) features extracted from glomerular and non-glomerular regions are used to train a support vector machine (SVM), deployed in tandem with a deep convolutional neural network trained for glomerular recognition. Using 5 Intel(R) Core(TM) i7-4790 CPUs with 40 GB RAM, the method takes ~2 min to extract glomeruli from a biopsy WSI (~106 pixels).
O. Simon, R. Yacoub, S. Jain, J. E. Tomaszewski, and P. Sarder, “Multi-radial LBP features as a tool for rapid glomerular detection and assessment in whole slide histopathology images,” Scientific Reports – Nature, vol. 8, pp. 2032:1–11, Feb. 2018.