The glomerulus is the primary compartment of blood filtration in the kidney. It is a sphere of bundled, fenestrated capillaries that selectively allows solute loss. Structural damages to glomerular micro-compartments lead to physiological failures which influence filtration efficacy. The sole way to confirm glomerular structural damage in renal pathology is by examining histopathological or immunofluorescence stained needle biopsies under a light microscope. However, this method is extremely tedious and time consuming, and requires manual scoring on the number and volume of structures. Computational image analysis is the perfect tool to ease this burden. The major obstacle to development of digital histopathological quantification protocols for renal pathology is the extreme heterogeneity present within kidney tissue. Here we present an automated computational pipeline to 1) segment glomerular compartment boundaries and 2) quantify features of compartments, in healthy and diseased renal tissue. The segmentation involves a two stage process, one step for rough segmentation generation and another for refinement. Using a Naïve Bayesian classifier on the resulting feature set, this method was able to distinguish pathological stage IIa from III with 0.89/0.93 sensitivity/specificity and stage IIb from III with 0.7/0.8 sensitivity/specificity, on n = 514 glomeruli taken from n = 13 human biopsies with diagnosed diabetic nephropathy, and n = 5 human renal tissues with no histological abnormalities. Our method will simplify computational partitioning of glomerular micro-compartments and subsequent quantification. We aim for our methods to ease manual labor associated with clinical diagnosis of renal disease.