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.
Highlights
Medical image processing has become more and more important in diagnosis with the development of medical imaging and computer technique
This paper presents an algorithm for the automatic detection of white blood cells (WBCs) embedded in complicated and cluttered smear images that considers the complete process as a multiellipse detection problem
Guided by the values of such function, the set of encoded candidate ellipses are evolved using the differential evolution (DE) algorithm so that they can fit into the WBCs which are enclosed within the edge map of the smear image
Summary
Medical image processing has become more and more important in diagnosis with the development of medical imaging and computer technique. By using a different approach, in [4], Wu et al developed an iterative Otsu method based on the circular histogram for leukocyte segmentation According to such technique, the smear images are processed in the huesaturation-intensity (HSI) space by considering that the hue component contains most of the WBC information. As an alternative to traditional techniques, the problem of ellipse detection has been handled through optimization methods They have demonstrated to give better results than those based on the HT and random sampling with respect to accuracy and robustness [13]. The main contribution of this study is the proposal of a new WBC detector algorithm that efficiently recognizes WBC under different complex conditions while considering the whole process as an ellipse detection problem.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
