White Blood Cells Counting Via Vector Field Convolution Nuclei Segmentation

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 leukoc ytes. 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.