Authors:
Simone Porcu
;
Andrea Loddo
;
Lorenzo Putzu
and
Cecilia Di Ruberto
Affiliation:
University of Cagliari, Italy
Keyword(s):
WBC Count, Detection, Segmentation, Vector Field Convolution, Mathematical Morphology.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Medical Image Applications
;
Segmentation and Grouping
;
Shape Representation and Matching
Abstract:
Haematological procedures like analysis, counting and classification of White Blood Cells (WBCs) are very
helpful in the medical field, in order to recognize a pathology, e.g., WBCs analysis leukaemia correlation.
Expert technicians manually perform these procedures, therefore, they are influenced by their tiredness and
subjectivity. Their automation is still an open issue. Our proposal aims to replicate every single step of
the haematologists’ job with a semi-automatic system. The main targets of this work are to decrease the
time needed for an analysis and to improve the efficiency of the procedure. It is based on the Vector Field
Convolution (VFC) to describe cells edges, going beyond more classic methods like the active contour model.
This approach is crucial to face the WBCs clumps and overlaps segmentation issue. To sum up, we defined
a system that is able to recognise the leukocytes, to differentiate them from the other blood cells and, finally,
to divide the overlapping leuko
cytes. Experimental results obtained on three public datasets showed that the
method is accurate and robust, outperforming the state of the art methods for cells clumps identification and
cells counting.
(More)