Abstract:
The most widely used laboratory confirmation technique for malaria is visual inspection
of Giemsa stained blood smears on microscope. A detection and counting method for
malaria infected blood cells in a colour (RGB) microscopic image
was
developed with
th
e help
of
machine vision and artificial neural networks (ANN). The developed system
is capable of detecting individual blood cells in the image and recognized them as
malaria
infected or
non
-
infected. The system is capable of producing the number of blood
cells in
each category, which
can
be
use
d
as
an indicator of severity of infection. The system was
trained for 40 blood cells (from seven images) manually marking them as infected or
non
-
infected
, and 120 blood cells (from 15 images) were used to test the
system. The
sensitivity and the specificity of the system for that data set
was
found to be 90.0 % and
95.7 % respectively
for the images of blood cells of malaria infected and uninfected by
Plasmodium falciparum parasites.
