Akramifard Hamid, Firouzmand Mohammad, Moghadam Reza Askari
Faculty of Computer Engineering and IT University of Payame Nour, Tehran, IR Iran.
J Med Signals Sens. 2012 Jul;2(3):169-75.
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.
本文提出了一种从血液显微图像中提取白细胞（WBCs）、识别白细胞并对各类白细胞进行计数的方法。在医学诊断中，通过检查白细胞数量并与正常数量进行比较是一项新挑战，本文对此进行了讨论。在回顾了从血液学图像中提取白细胞的方法后，由于人工神经网络（ANNs）在分类方面具有很高的适用性，我们决定使用这种有效方法对白细胞进行分类，并且由于复值神经网络（CVNNs）与实值神经网络相比具有高速和稳定收敛的特点，我们将其用于分类目的。在所介绍的方法中，首先借助RGB颜色系统提取白细胞。接着，利用各类血细胞的特征及其配色方案，提取归一化特征向量，并将其发送到复值反向传播神经网络进行分类。最后，结果以各类白细胞数量的形式输出。尽管所用图像质量较低，但我们的方法通过CVNNs在提取和识别白细胞方面具有很高的准确性，因此，其在高质量图像上的结果肯定是可以接受的。这里使用的复值神经网络的学习时间明显少于实值神经网络。
