Ilhan Hamza Osman, Serbes Gorkem, Aydin Nizamettin
Department of Computer Engineering, Yildiz Technical University, Turkey.
Department of Biomedical Engineering, Yildiz Technical University, Turkey.
Comput Biol Med. 2020 Jul;122:103845. doi: 10.1016/j.compbiomed.2020.103845. Epub 2020 Jun 6.
Sperm Morphology is the key step in the assessment of sperm quality. Due to the effect of misleading human factors in manual assessments, computer-based techniques should be employed in the analysis. In this study, a computation framework including multi-stage cascade connected preprocessing techniques, region based descriptor features, and non-linear kernel SVM based learning is proposed for the classification of any stained sperm images for the assessment of the morphology. The proposed framework was evaluated on two sperm morphology datasets: the Human Sperm Head Morphology dataset (HuSHeM) and Sperm Morphology Image Data Set (SMIDS). The results indicate that cascading the preprocessing techniques used in the proposed framework, such as wavelet based local adaptive de-noising, modified overlapping group shrinkage, image gradient, and automatic directional masking, increased the classification accuracy by 10% and 5% for the HuSHeM and SMIDS, respectively. The proposed framework results in better overall accuracy than most state-of-the-art methods, while having significant advantages, such as eliminating the exhaustive manual orientation and cropping operations of the competitors with reasonable rates of consumption of time and source.
精子形态学是评估精子质量的关键步骤。由于人工评估中存在误导性人为因素的影响，在分析过程中应采用基于计算机的技术。在本研究中，提出了一个计算框架，包括多阶段级联的预处理技术、基于区域的描述符特征以及基于非线性核支持向量机的学习方法，用于对任何染色精子图像进行分类，以评估其形态学。该框架在两个人类精子形态学数据集上进行了评估：人类精子头部形态学数据集（HuSHeM）和精子形态学图像数据集（SMIDS）。结果表明，在所提出的框架中级联使用的预处理技术，如基于小波的局部自适应去噪、改进的重叠组收缩、图像梯度和自动方向掩蔽，分别使HuSHeM和SMIDS的分类准确率提高了10%和5%。所提出的框架比大多数现有方法具有更高的总体准确率，同时具有显著优势，例如以合理的时间和资源消耗率消除了竞争对手的详尽手动定向和裁剪操作。
