Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
504256 | Computerized Medical Imaging and Graphics | 2011 | 11 Pages |
Abstract
This paper proposes image processing algorithms to recognize five types of white blood cells in peripheral blood automatically. First, a method based on Gram–Schmidt orthogonalization is proposed along with a snake algorithm to segment nucleus and cytoplasm of the cells. Then, a variety of features are extracted from the segmented regions. Next, most discriminative features are selected using a Sequential Forward Selection (SFS) algorithm and performances of two classifiers, Artificial Neural Network (ANN) and Support Vector Machine (SVM), are compared. The results demonstrate that the proposed methods are accurate and sufficiently fast to be used in hematological laboratories.
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Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Seyed Hamid Rezatofighi, Hamid Soltanian-Zadeh,