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Leukemia means blood cancer which is featured by the uncontrolled and abnormal production of white blood cells (leukocytes) by the bone marrow in the blood. Analyzing microscopic blood cell images, diseases can be identified and diagnosed early. Hematologist are using technique of image processing to analyze, detect and identify leukemia types in patients recently. Detection through images is fast and cheap method as there is no special need of equipment for lab testing. In this paper, we proposed a method of detection of leukemia in patients from microscopic white blood cell images. We have focused on the changes in the geometry of cells and statistical parameters like mean and standard deviation which separates white blood cells from other blood components using processing tools like MATLAB and LabVIEW. Images processing steps like image enhancement, image segmentation and feature extraction are applied on microscopic images.

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A new non-adaptive Connectionist Architecture based Feature Extractor (CAFE) for English alphabetic patterns is presented here. Two different adaptive connectionist networks, viz. Multi-layer back propagation network (MBPN) andCounter propagation network (CPN) have been implemented for classification of the patterns and their performance analysis has been reported. The system is tolerant to translation and deformation and has been observed to classify noisy and distorted patterns correctly.

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