  creating automated processes in different areas of medical science with the
application of engineering tools is a highly growing field over recent decades.
In this context, many medical image processing and analyzing researchers use
worthwhile methods in artificial intelligence, which can reduce necessary human
power while increases accuracy of results. Among various medical images, blood
microscopic images play a vital role in heart failure diagnosis, e.g., blood
cancers. The prominent component in blood cancer diagnosis is white blood cells
(WBCs) which due to its general characteristics in microscopic images sometimes
make difficulties in recognition and classification tasks such as non-uniform
colors/illuminances, different shapes, sizes, and textures. Moreover,
overlapped WBCs in bone marrow images and neighboring to red blood cells are
identified as reasons for errors in the classification task. In this paper, we
have endeavored to segment various parts in medical images via Na\"ive Bayes
clustering method and in next stage via TSLDA classifier, which is supplied by
features acquired from covariance descriptor results in the accuracy of 98.02%.
It seems that this result is delightful in WBCs recognition.
