Study on Automatic Segmenation and Classification of White Blood Cell Image

Project: National Science and Technology CouncilNational Science and Technology Council Academic Grants

Project Details


The number and classification of white blood cells can provide important information in disease diagnosis. At present, white blood cells can be inspected by a hematology analyzer, but, not all hospitals can afford this expensive instrument. Furthermore, if the hematology analyzer shows an alarm signal about an inspection, the inspection must be check again in manual. However, manual white blood cell analysis is time consuming and exhaustive. Hence, we need a well-designed white blood cell classification CAD system to assist the inexperience physicians to avoid misdiagnosis and speedup the inspections. The accuracy of a CAD system is related to the data mining techniques. Data mining techniques have been used in classification problems. The support vector machine (SVM), artificial neural network (ANN), and decision trees (DT) are popular among them and can be applied to various areas. Nevertheless, different problems may require different parameters when applying SVM, ANN and DT. If parameters didn’t setting well, it will obtain unsatisfied result. On the other hand, a dataset may contain many features; however, not all features are beneficial for classification. The features may contain false correlations, which hinder the process of detecting process. Without feature selection, the classification accuracy rate may be worse due to the noises or too many dirty features. In most research either parameters turning or feature selection is used to improve classification accuracy rate. Therefore, the artificial immune system algorithm (AIS) is proposed to select the beneficial subset of features and to obtain the better parameters of SVM, ANN and DT, which will result in a better classification. The above data mining techniques has its own advantages and disadvantages and the suitability will influenced by the characteristic of problem. If these techniques can work together, it is expected that the better result can be obtained. This is so-called ensemble architecture. Therefore, this proposal is plan to use the ensemble architecture to further enhance the classification accuracy rate.

Project IDs

Project ID:PB10007-2314
External Project ID:NSC100-2221-E182-003
Effective start/end date01/08/1131/07/12


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