Rehman Amjad, Abbas Naveed, Saba Tanzila, Mahmood Toqeer, Kolivand Hoshang
College of Computer and Information Systems, Al Yamamah University, Riyadh, 11512, Saudi Arabia.
Computer Science Department Islamia College, University Peshawar, Pakistan.
Microsc Res Tech. 2018 Jul;81(7):737-744. doi: 10.1002/jemt.23030. Epub 2018 Apr 10.
Splitting the rouleaux RBCs from single RBCs and its further subdivision is a challenging area in computer-assisted diagnosis of blood. This phenomenon is applied in complete blood count, anemia, leukemia, and malaria tests. Several automated techniques are reported in the state of art for this task but face either under or over splitting problems. The current research presents a novel approach to split Rouleaux red blood cells (chains of RBCs) precisely, which are frequently observed in the thin blood smear images. Accordingly, this research address the rouleaux splitting problem in a realistic, efficient and automated way by considering the distance transform and local maxima of the rouleaux RBCs. Rouleaux RBCs are splitted by taking their local maxima as the centres to draw circles by mid-point circle algorithm. The resulting circles are further mapped with single RBC in Rouleaux to preserve its original shape. The results of the proposed approach on standard data set are presented and analyzed statistically by achieving an average recall of 0.059, an average precision of 0.067 and F-measure 0.063 are achieved through ground truth with visual inspection.
将聚集的红细胞与单个红细胞分离并进一步细分，在血液的计算机辅助诊断中是一个具有挑战性的领域。这种现象应用于全血细胞计数、贫血、白血病和疟疾检测。目前已有几种自动化技术用于此任务，但都面临分割不足或过度的问题。当前的研究提出了一种精确分离聚集红细胞（红细胞链）的新方法，这种细胞在薄血涂片图像中经常出现。因此，本研究通过考虑聚集红细胞的距离变换和局部最大值，以一种现实、高效且自动化的方式解决了聚集红细胞的分割问题。通过将聚集红细胞的局部最大值作为圆心，利用中点圆算法绘制圆圈来分离聚集红细胞。得到的圆圈进一步与聚集体中的单个红细胞进行映射，以保留其原始形状。通过目视检查的真值，给出了该方法在标准数据集上的结果，并进行了统计分析，平均召回率达到0.059，平均精度达到0.067，F值达到0.063。
