Abstract:
Leukemia is a life-threatening disease. So far diagnosing of leukemia is manually carried out by the
Hematologists that is time-consuming and error-prone. The crucial problem is leukocytes’ nuclei segmentation precisely.
This paper presents a novel technique to solve the problem by applying statistical methods of Gaussian mixture model
through expectation maximization for the basic and challenging step of leukocytes’ nuclei segmentation. The proposed
technique is being tested on a set of 365 images and the segmentation results are validated both qualitatively and
quantitatively with current state-of-the-art methods on the basis of ground truth data (manually marked images by
medical experts). The proposed technique is qualitatively compared with current state-of-the-art methods on the basis of
ground truth data through visual inspection on four different grounds. Finally, the proposed technique quantitatively
achieved an overall segmentation accuracy, sensitivity and precision of 92.8%, 93.5% and 98.16% respectively while an
overall F-measure of 95.75%
