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SHORT COMMUNICATION
Year : 2012  |  Volume : 2  |  Issue : 3  |  Page : 169-175

Extracting, recognizing, and counting white blood cells from microscopic images by using complex-valued neural networks


1 Faculty of Computer Engineering and IT University of Payame Nour, Tehran, Iran
2 Iranian Research Organization for Science and Technology (IROST), Tehran, Iran
3 Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran

Correspondence Address:
Hamid Akramifard
Faculty of Computer Engineering and IT University of Payame Nour, Tehran
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2228-7477.112144

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In this paper a method related to extracting white blood cells (WBCs) from blood microscopic images and recognizing them and counting each kind of WBCs is presented. In medical science diagnosis by check the number of WBCs and compared with normal number of them is a new challenge and in this context has been discussed it. After reviewing the methods of extracting WBCs from hematology images, because of high applicability of artificial neural networks (ANNs) in classification we decided to use this effective method to classify WBCs, and because of high speed and stable convergence of complex-valued neural networks (CVNNs) compare to the real one, we used them to classification purpose. In the method that will be introduced, first the white blood cells are extracted by RGB color system's help. In continuance, by using the features of each kind of globules and their color scheme, a normalized feature vector is extracted, and for classifying, it is sent to a complex-valued back-propagation neural network. And at last, the results are sent to the output in the shape of the quantity of each of white blood cells. Despite the low quality of the used images, our method has high accuracy in extracting and recognizing WBCs by CVNNs, and because of this, certainly its result on high quality images will be acceptable. Learning time of complex-valued neural networks, that are used here, was significantly less than real-valued neural networks.


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