Practical Handbook on Image Processing for Scientific and Technical Applications, Second Edition
Practical Handbook on Image Processing for Scientific and Technical Applications, Second Edition
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
A method based on multispectral imaging technique for White Blood Cell segmentation
Computers in Biology and Medicine
Recognition and classification of colon cells applying the ensemble of classifiers
Computers in Biology and Medicine
Acute lymphoblastic leukemia identification using blood smear images and a neural classifier
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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The early identification of leukemia form in cancer patients can greatly increase the likelihood of recovery. Diagnostic methods that distinguish among the disease's many forms are either costly or do not exist. Amongst the existing diagnostic methods are immune-phenotype and cytogenetic abnormality, which require time to obtain results and are costly to perform due to their requirement of well equipped laboratories. Thus, there is a need for fast and cost-effective method that results in the identification of the different leukemia forms or types. Therefore, we propose the use of morphological analysis of microscopic images of leukemic blood cells for the identification purpose. We present in this paper the first phase of an automated leukemia form identification system, which is the segmentation of infected cell images. The segmentation process provides two enhanced images for each blood cell; containing the cytoplasm and the nuclei regions. Unique features for each form of leukemia can then be extracted from the two images and used for identification.