An image processing approach for enumeration of leukemia infected cells for diagnosis
Keywords:
Leukemia, entropy, computerized diagnosis, threshold, connected component labeling, microscopic imagesAbstract
Image processing techniques are helped to improve the diagnosis of various diseases using medical images. Leukemia, a blood cancer, is one of the commonest malignancies affecting both adults and children. As per the reports, leukemia was the fifth and sixth cause of death in men (7%) and women (5.8%) with cancer, and it was the first cause of death in children with cancer between 1–4 and 5–14 years old, with 48.5% and 52.2% of deceases, respectively. Hematologists microscopically examine the blood under a light microscope for diagnosis. This process is very tedious, time-consuming, and not suitable for analyzing a large number of cells. The enumeration of infected cells for the diagnosis of leukemia will assist to overcome these drawbacks. Enumeration of blood cells plays a very important role in the health sector. In this paper, we have proposed the enumeration of leukemia-infected cell parasites in microscopic blood images. The infected parasites are detected by the proposed threshold method by maximizing the between-class variance and entropy of black and white pixels. The enumeration is carried out by connected component labeling technique from binary decision image obtained in maximizing entropy.
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