Fariba Shaker1, S Amirhassan Monadjemi2, Javad Alirezaie3, Ahmad Reza Naghsh-Nilchi4. 1. Department of AI, Faculty of Computer Engineering, University of Isfahan, Isfahan, 81746, Iran. Electronic address: f.shaker@eng.ui.ac.ir. 2. Department of AI, Faculty of Computer Engineering, University of Isfahan, Isfahan, 81746, Iran. Electronic address: monadjemi@eng.ui.ac.ir. 3. Department of Electrical and Computer Engineering, Ryerson University, Toronto, M5B 2K3, Canada. Electronic address: javad@ryerson.ca. 4. Department of AI, Faculty of Computer Engineering, University of Isfahan, Isfahan, 81746, Iran. Electronic address: nilchi@eng.ui.ac.ir.
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.
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.
Authors: Christopher McCallum; Jason Riordon; Yihe Wang; Tian Kong; Jae Bem You; Scott Sanner; Alexander Lagunov; Thomas G Hannam; Keith Jarvi; David Sinton Journal: Commun Biol Date: 2019-07-03
Authors: Steven A Hicks; Jorunn M Andersen; Oliwia Witczak; Vajira Thambawita; Pål Halvorsen; Hugo L Hammer; Trine B Haugen; Michael A Riegler Journal: Sci Rep Date: 2019-11-14 Impact factor: 4.379