Ghane Narjes, Vard Alireza, Talebi Ardeshir, Nematollahy Pardis
Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine and Student Research Center, Isfahan, Iran.
Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine and Medical Image and Signal Processing Research Center, Isfahan, Iran.
J Med Signals Sens. 2017 Apr-Jun;7(2):92-101.
Recognition of white blood cells (WBCs) is the first step to diagnose some particular diseases such as acquired immune deficiency syndrome, leukemia, and other blood-related diseases that are usually done by pathologists using an optical microscope. This process is time-consuming, extremely tedious, and expensive and needs experienced experts in this field. Thus, a computer-aided diagnosis system that assists pathologists in the diagnostic process can be so effective. Segmentation of WBCs is usually a first step in developing a computer-aided diagnosis system. The main purpose of this paper is to segment WBCs from microscopic images. For this purpose, we present a novel combination of thresholding, k-means clustering, and modified watershed algorithms in three stages including (1) segmentation of WBCs from a microscopic image, (2) extraction of nuclei from cell's image, and (3) separation of overlapping cells and nuclei. The evaluation results of the proposed method show that similarity measures, precision, and sensitivity respectively were 92.07, 96.07, and 94.30% for nucleus segmentation and 92.93, 97.41, and 93.78% for cell segmentation. In addition, statistical analysis presents high similarity between manual segmentation and the results obtained by the proposed method.
识别白细胞是诊断某些特定疾病（如获得性免疫缺陷综合征、白血病和其他血液相关疾病）的第一步，这一过程通常由病理学家使用光学显微镜来完成。这个过程既耗时、极其繁琐又昂贵，并且需要该领域经验丰富的专家。因此，一个在诊断过程中协助病理学家的计算机辅助诊断系统会非常有效。白细胞分割通常是开发计算机辅助诊断系统的第一步。本文的主要目的是从微观图像中分割白细胞。为此，我们在三个阶段提出了一种新颖的阈值处理、k均值聚类和改进的分水岭算法的组合，包括（1）从微观图像中分割白细胞，（2）从细胞图像中提取细胞核，以及（3）分离重叠的细胞和细胞核。所提方法的评估结果表明，细胞核分割的相似性度量、精度和灵敏度分别为92.07%、96.07%和94.30%，细胞分割的相似性度量、精度和灵敏度分别为92.93%、97.41%和93.78%。此外，统计分析表明手动分割与所提方法获得的结果之间具有高度相似性。
