Floating search methods in feature selection
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A texture approach to leukocyte recognition
Real-Time Imaging - Special issue on imaging in bioinformatics: Part III
A method based on multispectral imaging technique for White Blood Cell segmentation
Computers in Biology and Medicine
A New Approach to White Blood Cell Nucleus Segmentation Based on Gram-Schmidt Orthogonalization
ICDIP '09 Proceedings of the International Conference on Digital Image Processing
System-level training of neural networks for counting white bloodcells
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections
IEEE Transactions on Information Technology in Biomedicine
A new preprocessing approach for cell recognition
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
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An automatic system which is capable of recognizing white blood cells can assist hematologists in the diagnosis of many diseases. In this paper, we propose a new system based on image processing techniques in order to recognize five types of white blood cells in the peripheral blood. To segment nucleus and cytoplasm, a Gram-Schmidt orthogonalization method and a snake algorithm are applied, respectively. Moreover, three kinds of features are extracted from the segmented areas and two groups of textural features extracted by Local Binary Pattern (LBP) and co-occurrence matrix are evaluated. Best features are selected using a Sequential Forward Selection (SFS) algorithm and performances of two classifiers, ANN and SVM, are compared. In this application, the best result is obtained using LBP as the textural feature and SVM as the classifier. In sum, the results demonstrate that the methods are accurate and fast enough to execute in hematological laboratories.