The branch of pathology concerned with excess blood serum proteins being excreted in the urine pays particular attention to the glomerulus, a small intertwined bunch of capillaries located at the beginning of the nephron. Normal glomeruli allow moderate amount of blood proteins to be filtered; proteinuric glomeruli allow large amount of blood proteins to be filtered. Diagnosis of proteinuric diseases requires time intensive manual examination of the structural compartments of the glomerulus from renal biopsies. Pathological examination includes cellularity of individual compartments, Bowman’s and luminal space segmentation, cellular morphology, glomerular volume, capillary morphology, and more. Long examination times may lead to increased diagnosis time and/or lead to reduced precision of the diagnostic process. Automatic quantification holds strong potential to reduce renal diagnostic time. We have developed a computational pipeline capable of automatically segmenting relevant features from renal biopsies. Our method first segments glomerular compartments from renal biopsies by isolating regions with high nuclear density. Gabor texture segmentation is used to accurately define glomerular boundaries. Bowman’s and luminal spaces are segmented using morphological operators. Nuclei structures are segmented using color deconvolution, morphological processing, and bottleneck detection. Average computation time of feature extraction for a typical biopsy, comprising of ~12 glomeruli, is ∼69 s using an Intel(R) Core(TM) i7-4790 CPU, and is ~65X faster than manual processing. Using images from rat renal tissue samples, automatic glomerular structural feature estimation was reproducibly demonstrated for 15 biopsy images, which contained 148 individual glomeruli images. The proposed method holds immense potential to enhance information available while making clinical diagnoses.