Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
A texture approach to leukocyte recognition
Real-Time Imaging - Special issue on imaging in bioinformatics: Part III
Support Vector Machines Applied to White Blood Cell Recognition
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Leukocyte Segmentation in Giemsa-stained Image of Peripheral Blood Smears Based on Active Contour
ICSPS '09 Proceedings of the 2009 International Conference on Signal Processing Systems
Leukocyte Recognition Using EM-Algorithm
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Information Technology in Biomedicine
Edge Enhancement Nucleus and Cytoplast Contour Detector of Cervical Smear Images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear operator for oriented texture
IEEE Transactions on Image Processing
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The number or ratio of leukocytes in the blood is often an indicator of diseases. The leukocyte differential count is the process of diagnosing diseases by counting the number and ratio of leukocytes in the blood. However, leukocytes are usually manually classified in laboratories by using microscopes. It is a painstaking and subjective task for biologists. In order to improve the recognition accuracy and decrease the time consuming, an automatic leukocyte detective system is essential for assisting biologists in diagnosing diseases. This paper provides a method to detect and recognize leukocyte automatically. In general, leukocytes are categorized into five groups, including lymphocytes, monocytes, eosinophils, basophils and neutrophils. Each category plays a different role in the human immune system. The nucleus contains the main composition of a leukocyte and it can be used as an important feature to classify a disease. In this paper, the nuclei are used to identify five types of leukocyte. The leukocyte cell nucleus enhancer is proposed to segment the region we are interested in by enhancing the region of the leukocyte nucleus and suppressing the other region of the blood smear images. The segmental threshold of leukocyte nuclei is based on the Otsu's method. In addition, the erythrocyte size is estimated not only to assist the filtering out of the non-leukocyte objects but also to classify the leukocytes. Then the co-occurrence matrix is used as a textural measure of segmented images. In addition to the texture measure, we take into account of shape measures. In the recognition steps, we reduce features by principle component analysis (PCA) to obtain suitable feature to distinguish the five types of leukocytes. The genetic algorithm based k-means clustering approach is used to classify the five kinds of leukocyte in the reduced dimensions. The experimental results show that even though only leukocyte nucleus features are used for classification in our method, we achieve a high and promised accurate recognition rate.