title: An improved computer vision method for white blood cells detection.
abstract: The automatic detection of white blood cells (WBCs) still remains as an unsolved issue in medical imaging. The analysis of WBC images has engaged researchers from fields of medicine and computer vision alike. Since WBC can be approximated by an ellipsoid form, an ellipse detector algorithm may be successfully applied in order to recognize such elements. This paper presents an algorithm for the automatic detection of WBC embedded in complicated and cluttered smear images that considers the complete process as a multiellipse detection problem. The approach, which is based on the differential evolution (DE) algorithm, transforms the detection task into an optimization problem whose individuals represent candidate ellipses. An objective function evaluates if such candidate ellipses are actually present in the edge map of the smear image. Guided by the values of such function, the set of encoded candidate ellipses (individuals) are evolved using the DE algorithm so that they can fit into the WBCs which are enclosed within the edge map of the smear image. Experimental results from white blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique in terms of its accuracy and robustness.
authors.affiliation: Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenida Revolución 1500, 44430 Guadalajara, JAL, Mexico. erik.cuevas@cucei.udg.mx
authors.forename: Erik
authors.initials: E
authors.lastname: Cuevas
authors.forename: Margarita
authors.initials: M
authors.lastname: Díaz
authors.forename: Miguel
authors.initials: M
authors.lastname: Manzanares
authors.forename: Daniel
authors.initials: D
authors.lastname: Zaldivar
authors.forename: Marco
authors.initials: M
authors.lastname: Perez-Cisneros
mesh_terms: D000465:Algorithms; D001331:Automation; D048430:Cell Shape; D019295:Computational Biology; D003198:Computer Simulation; D006403:Hematologic Tests / Q000706:statistics & numerical data; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007962:Leukocytes / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data