A dictionary learning approach for human sperm heads classification. (1st December 2017)

• Record Type: Journal Article
• Authors: Shaker, Fariba; Monadjemi, S. Amirhassan; Alirezaie, Javad; Naghsh-Nilchi, Ahmad Reza
• Is Part Of: Computers in biology and medicine. Volume 91(2017)
• Page Start: 181
• Page End: 190
• Publication Date: 2017-12-01
• Subjects: Sperm head classification -- Sperm abnormality -- Sperm morphology -- Dictionary learning -- Sparse representation -- Infertility; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.10.009
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background and objective: To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the intra-class differences and inter-class similarities of class objects. In this research, a Dictionary Learning (DL) technique is utilized to construct a dictionary of sperm head shapes. This dictionary is used to classify the sperm heads into four different classes. Methods: Square patches are extracted from the sperm head images. Columnized patches from each class of sperm are used to learn class-specific dictionaries. The patches from a test image are reconstructed using each class-specific dictionary and the overall reconstruction error for each class is used to select the best matching class. Average accuracy, precision, recall, and F-score are used to evaluate the classification method. The method is evaluated using two publicly available datasets of human sperm head shapes. Results: The proposed DL based method achieved an average accuracy of 92.2% on the HuSHeM dataset, and an average recall of 62% on the SCIAN-MorphoSpermGS dataset. The results show a significant improvement compared to a previously published shape-feature-based method. We have achieved high-performance results. In addition, our proposed approach offers a more balanced classifier in which all four classes are recognized with high precision and recall. Conclusions: In this paper, we use a Dictionary Learning approach in classifying human sperm heads. It is shown that the Dictionary Learning method is far more effective in classifying human sperm heads than classifiers using shape-based features. Also, a dataset of human sperm head shapes is introduced to facilitate future research. Highlights: Dictionary learning technique is adapted for classification of human sperm heads. Four classes of sperm shapes are recognized with high F-score. The proposed method was evaluated using two publically available datasets. The proposed method achieved 92.2% accuracy. The dataset of human sperm head morphology (HuSHeM) is introduced.