Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenida Revolución 1500, 44430 Guadalajara, JAL, Mexico.
Comput Math Methods Med. 2013;2013:137392. doi: 10.1155/2013/137392. Epub 2013 May 19.
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.
白细胞（WBC）的自动检测在医学成像中仍然是一个未解决的问题。WBC 图像的分析吸引了医学和计算机视觉领域的研究人员。由于 WBC 可以近似为一个椭圆形，因此可以成功地应用椭圆检测算法来识别此类元素。本文提出了一种用于自动检测复杂和杂乱涂片图像中嵌入的 WBC 的算法，该算法将整个过程视为多椭圆检测问题。该方法基于差分进化（DE）算法，将检测任务转化为一个优化问题，其个体代表候选椭圆。一个目标函数评估候选椭圆是否实际存在于涂片图像的边缘图中。在该函数值的指导下，使用 DE 算法对编码的候选椭圆（个体）集进行进化，以便它们能够适应位于涂片图像边缘图内的 WBC。实验结果包括具有不同复杂程度的白细胞图像，以验证所提出技术在准确性和鲁棒性方面的效率。
