Implementation of a Semi-automatic Tool for Analysis of TEM Images of Kidney Samples

Abstract: Glomerular disease is a cause for chronic kidney disease and it damages the function of the kidneys. One symptom of glomerular disease is proteinuria, which means that large amounts of protein are emerged in the urine. To be more objective,transmission electron microscopy (TEM) imaging of tissue biopsies of kidney are used when measuring proteinuria. Foot process effacement (FPE), which is defined as less than1 ”slit”(gap)/micrometer at the glomerular basement membrane (GBM). Measuring FPE is one way to detect proteinuria using kidney TEM images, this technique is a time-consuming task and used to be measured manually by an expert. This master thesis project aims at developing a semi-automatic way to detect the FPE patients as well as a graphic user interface (GUI) to make the methods and results easily accessible for the user. To compute the slits/micrometer for each image, the GBM needs to be segmented from the background. The proposed work flow combines various filters and mathematical morphology to obtain the outer contour of the GBM. The outer contour is then smoothed, and unwanted parts are removed based on distance information and angle differences between points on the contour. The length is then computed by weighted chain code counts. At last, an iterative algorithm is used to locate the positions of the "slits" using both gradient and binary information of the original images. If necessary, the result from length measurement and "slits" counting can be manually corrected  by the user. A tool for manual measurement is also provided as an option. In this case, the user can add anchor points on the outer contour of the GBM and then the length is automatically measured and "slit" locations are detected. For very difficult images, the users can also mark all "slits" locations by hand. To evaluate the performance and the accuracy, data from five patients are tested,for each patient six images are available. The images are 2048 by 2048 gray-scale indexed 8 bit images and the scale is 0.008 micrometer/pixel. The one FPE patient in the dataset is successfully distinguished.