||Glomerular disease is a cause of chronic kidney disease and it damages
the function of kidney.
One symptom of glomerular disease is proteinuria, which means that
large amounts of
protein are emerged in the urine. To detect proteinuria, one way is to
detect foot process effacement(
FPE). FPE is defined as less than 1 slits/um at the glomerular
from the expert. Measuring it in TEM images is a time-consuming task
which used to be measured
manually by an expert.
This master’s thesis project aims at developing a semi-automatic way
to distinct the FPE
patients as well as a GUI to make the methods and results accessible
for the user.
To compute the slits/mm 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 position of the ”slits” using local information.
For each iteration, both binary
image information and gradient information are used to compute
candidate ”slits” locations.
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
and ”slits” locations are detected. For very difficult images, the
user can also mark all ”slits”
location by hand.
To evaluate the performance and the accuracy, data from five patients
are tested, and for each
patient six images are available. The images are 2048 2048 gray-scale
indexed 8 bit images
and the scale was 0.008 um/ pixel. The FPE patient is successfully