A Novel Method for Segmentation of Leukocyte Nuclei Based on Color Transformation

Acute lymphoblastic leukemia is one of the most common hematologic malignancies among children, caused by uncontrolled growth of leukocytes. Since the main hallmarks of the disease is not specific, a considerable number of patients have been being misdiagnosed. Early diagnosis of the disease is usually made by morphological investigation of leukocytes under microscope. In light of the facts that decrease in cytoplasm-to-nucleus ratio is one of the main indicators of cancerous cells, and an accurate segmentation phase will lead to extraction of representative features, segmentation step is acknowledged as being crucial in design of a computer aided diagnosis (CAD). Previous researches have utilized standard, pre-defined color spaces, such as CMYK, for the segmentation of leukocyte nucleus. However, since these color spaces were not designed specifically for the segmentation task, they cannot extract nuclei efficiently. Thus, in this paper, by using feed forward neural networks, we propose a color transformation method, which maps RGB to a special 2D space. Design parameters of the neural network are tuned by using genetic algorithm. In the new space, nuclei of the leukocytes have the highest discrimination against background, so by using Otsu thresholding, one can extract the nuclei easily. Efficacy of the proposed method is evaluated by using ALL-IDB2 dataset, which is publicly available. Our obtained Dice similarity coefficient is higher than that of the other newly devised algorithms, showing its great performance.