Related Experiment Video
Updated: Jun 21, 2026

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
A semi-automatic algorithm for measurement of basement membrane thickness in kidneys in electron microscopy images
Hai-Shan Wu1, Steven Dikman, Joan Gil
1Department of Pathology, Box 1194, Mount Sinai School of Medicine, One Gustave L. Levy Place, New York, NY 10029, USA. haishan.wu@mssm.edu
Abstract:
In this paper, we present a semi-automatic algorithm for measurement of the glomerular basement membrane thickness in electron microscopy kidney images. A string of sparsely spaced points are manually inputted along the central line of the basement membrane (lamina densa) to be measured. The gaps between successive input points are lineally interpolated. A nonlinear mapping is applied to straighten the curved central line. Two distance functions of edges to the central line are constructed. The smooth envelope lines are obtained by repetitive applications of a linear low-pass filtering followed by a comparing and selecting process. The boundaries of the glomerular basement membrane are obtained from the inverse mapping of the envelope functions. The average basement membrane thickness is estimated as the ratio of the basement membrane area to the length of the central line.

