  Leukemia is a hematologic cancer which develops in blood tissue and triggers
rapid production of immature and abnormal shaped white blood cells. Based on
statistics it is found that the leukemia is one of the leading causes of death
in men and women alike. Microscopic examination of blood sample or bone marrow
smear is the most effective technique for diagnosis of leukemia. Pathologists
analyze microscopic samples to make diagnostic assessments on the basis of
characteristic cell features. Recently, computerized methods for cancer
detection have been explored towards minimizing human intervention and
providing accurate clinical information. This paper presents an algorithm for
automated image based acute leukemia detection systems. The method implemented
uses basic enhancement, morphology, filtering and segmenting technique to
extract region of interest using k-means clustering algorithm. The proposed
algorithm achieved an accuracy of 92.8% and is tested with Nearest Neighbor
(KNN) and Naive Bayes Classifier on the data-set of 60 samples.
