title: Automatic recognition of five types of white blood cells in peripheral blood.
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.
authors.affiliation: Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran, Tehran 14395-515, Iran. h.tofighi@ece.ut.ac.ir
authors.forename: Seyed Hamid
authors.initials: SH
authors.lastname: Rezatofighi
authors.forename: Hamid
authors.initials: H
authors.lastname: Soltanian-Zadeh
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007962:Leukocytes / Q000145:classification*; D008853:Microscopy; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*