We present a rapid, scalable, and high throughput computational pipeline to accurately detect and segment the glomerulus from renal histopathology images with high precision and accuracy. Our proposed method integrates information from fluorescence and bright-field microscopy imaging of renal tissues. For computation, we exploit the simplicity, yet extreme robustness of Butterworth bandpass filter to extract the glomeruli by utilizing the information inherent in the renal tissue stained with immunofluorescence marker sensitive at blue emission wavelength as well as tissue auto-fluorescence. The resulting output is in-turn used to detect and segment multiple glomeruli within the fieldof-view in the same tissue section post-stained with histopathological stains. Our approach, optimized over 40 images, produced a sensitivity/specificity of 0.95/0.84 on n = 66 test images, each containing one or more glomeruli. The work not only has implications in renal histopathology involving diseases with glomerular structural damages, which is vital to track the progression of the disease, but also aids in the development of a tool to rapidly generate a database of glomeruli from whole slide images, essential for training neural networks. The current practice to detect glomerular structural damage is by the manual examination of biopsied renal tissues, which is laborious, time intensive and tedious. Existing automated pipelines employ complex neural networks which are computationally extensive, demand expensive highperformance hardware and require large expert-annotated datasets for training. Our automated method to detect glomerular boundary will aid in rapid extraction of glomerular compartmental features from large renal histopathological images.