Iranian Research Institute for Electrical Engineering, ACECR, Tehran, Islamic Republic of Iran.
Royan Institute, ACECR,Tehran, Islamic Republic of Iran.
Comput Methods Programs Biomed. 2018 Feb;154:173-182. doi: 10.1016/j.cmpb.2017.11.005. Epub 2017 Nov 14.
Proper recognition and tracking of microscopic sperm cells in video images are vital steps of male infertility diagnosis and treatment. The segmentation and detection of sperms in microscopic image analysis is a complicate process as a result of their small sizes, fast movements, and considerable collisions. Histogram-based thresholding schemes are very popular for this purpose, since they are quite fast and provide almost acceptable results. This paper proposes a combined method for sperm cells detection, which consists of a non-linear pre-processing stage, a histogram-based thresholding algorithm, and a tracking method based on an adaptive distance scheme. The results of conducted experiments verify the superiority of the proposed scheme with incorporated Kittler algorithm compared to the other competitive methods in the majority of cases.
在视频图像中正确识别和跟踪微观精子细胞是男性不育症诊断和治疗的关键步骤。由于精子细胞体积小、运动速度快且碰撞频繁，因此在微观图像分析中对精子细胞进行分割和检测是一个复杂的过程。基于直方图的阈值方案因其速度快且提供几乎可接受的结果而非常受欢迎。本文提出了一种用于精子细胞检测的组合方法，该方法由一个非线性预处理阶段、一个基于直方图的阈值算法和一个基于自适应距离方案的跟踪方法组成。所进行的实验结果验证了所提出的方案与其他竞争方法相比在大多数情况下的优越性，其中纳入了 Kittler 算法。
