Towards Prediction of Platelet Count and Classification of White Blood Cell Type, State and AML Stage from Blood Smears
Dejey, Arputha Regina A
Journal of Information Technology Review, Vol. 6, No. 2, 2015.
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Abstract:
Blood smear analysis plays an important role in the diagnosis of diseases. It is performed by doctors through visual examination of blood smears under microscope. But this is time consuming, tedious and susceptible to error. Hence automated analysis of blood smears is essential. The objective of the paper is fourfold: To determine (i) platelet count which
could be used as a preliminary screening for dengue (ii) the type and count of White Blood Cells(WBCs) (iii) the normal and abnormal WBCs and (iv) the stage of Acute Myeloid Leukemia (AML) if abnormal from blood smear. To determine the platelet count, mapping and gray level transformation are done prior to edge detection and morphology based segmentation. To
determine the type and count of WBC, the stage of AML and to determine whether WBC is normal or abnormal, threshold based segmentation is done followed by morphological operations to extract the structure of the nucleus within the blood smear. Features are extracted and further fuzzy classification is done. The experiment is conducted with real data set
comprising of blood smear images from Tirunelveli Medical College Hospital (TVMCH). Besides these, images available in
ASH Image Bank are also used. The results guarantee an accuracy of 100% for platelet count estimation and the classification
of abnormal and normal White Blood Cells (WBC) and an accuracy of 94% for WBC type and Acute Myeloid Leukemia (AML)
stage classification. The results are validated with the results of pathologist.