Leukemia is a blood cancer which is defined as an irregular augment of undeveloped white blood
cells called “blasts.” It develops in the bone marrow, which is responsible for blood cell generation
including leukocytes and white blood cells. The early diagnosis of leukemia greatly helps in the
treatment. Accordingly, researchers are interested in developing advanced and accurate automated
techniques for localizing such abnormal blood cells. Subsequently, image segmentation becomes an
important image processing stage for successful feature extraction and classification of leukemia in
further stages. It aims to separate cancer cells by segmenting the microscopic image into background
and cancer cells that are known as the region of interested (ROI). In this article, the cancer blood
cells were segmented using two separated clustering techniques, namely the K-means and Fuzzyc-means techniques. Then, the results of these techniques were compared to in terms of different
segmentation metrics, such as the Dice, Jac, specificity, sensitivity, and accuracy. The results proved
that the k-means provided better performance in leukemia blood cells segmentation as it achieved an
accuracy of 99.8% compared to 99.6% with the fuzzy c-means.