scholarly journals Quantitative Analysis of Locomotive Behavior of Human Sperm Head and Tail

2013 ◽  
Vol 60 (2) ◽  
pp. 390-396 ◽  
Author(s):  
Jun Liu ◽  
Clement Leung ◽  
Zhe Lu ◽  
Yu Sun
Keyword(s):  
Quantitative Analysis ◽  
Human Sperm ◽  
Sperm Head ◽  
Locomotive Behavior

scholarly journals Quantitative Analysis of Locomotive Behavior of Human Sperm Head and Tail

2013 ◽  
pp. 603-616
Author(s):  
Jun Liu ◽  
Zhe Lu ◽  
Clement Leung ◽  
Yu Sun
Keyword(s):  
Quantitative Analysis ◽  
Human Sperm ◽  
Sperm Head ◽  
Locomotive Behavior

scholarly journals Distribution of DNA damage in the human sperm nucleus: implications of the architecture of the sperm head

2020 ◽  
Vol 22 (4) ◽  
pp. 401
Author(s):  
MaríaPaz Herráez ◽  
Silvia González-Rojo ◽  
Cristina Fernández-Díez ◽  
Marta Lombó
Keyword(s):  
Dna Damage ◽  
Human Sperm ◽  
Sperm Nucleus ◽  
Sperm Head

Supervised Machine Learning Classification of Human Sperm Head Based on Morphological Features

2021 ◽  
pp. 177-191
Author(s):  
Natalia V. Revollo ◽  
G. Noelia Revollo Sarmiento ◽  
Claudio Delrieux ◽  
Marcela Herrera ◽  
Rolando González-José
Keyword(s):  
Machine Learning ◽  
Human Sperm ◽  
Sperm Head ◽  
Morphological Features ◽  
Supervised Machine Learning ◽  
Machine Learning Classification

Human sperm head vacuoles are physiological structures formed during the sperm development and maturation process

2012 ◽  
Vol 98 (2) ◽  
pp. 315-320 ◽  
Author(s):  
Atsushi Tanaka ◽  
Motoi Nagayoshi ◽  
Izumi Tanaka ◽  
Hiroshi Kusunoki
Keyword(s):  
Human Sperm ◽  
Sperm Head ◽  
Maturation Process ◽  
Sperm Development

Human sperm head morphometric distribution and its influence on human fertility

1998 ◽  
Vol 70 (5) ◽  
pp. 883-891 ◽  
Author(s):  
Nabil Aziz ◽  
Simon Fear ◽  
Clare Taylor ◽  
Charles R Kingsland ◽  
D.Iwan Lewis-Jones
Keyword(s):  
Human Sperm ◽  
Sperm Head ◽  
Human Fertility

Automatic classification of human sperm head morphology

2017 ◽  
Vol 84 ◽  
pp. 205-216 ◽  
Author(s):  
Violeta Chang ◽  
Laurent Heutte ◽  
Caroline Petitjean ◽  
Steffen Härtel ◽  
Nancy Hitschfeld
Keyword(s):  
Human Sperm ◽  
Automatic Classification ◽  
Sperm Head ◽  
Head Morphology

scholarly journals Bayesian Pixel Density Estimation Modeling to Detect Human Sperm Sample Image Based on Sperm Head Shape

SinkrOn ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 91-99
Author(s):  
Candra Zonyfar ◽  
Kiki Ahmad Baihaqi
Keyword(s):  
Density Estimation ◽  
Human Sperm ◽  
Sperm Head ◽  
Zernike Moment ◽  
Estimation Model ◽  
Computational Accuracy ◽  
Proposed Model ◽  
Normal Human ◽  
Bayesian Density Estimation ◽  
Hu Moment

Currently, there is a problem of the difficulty in classifying human sperm head sample images using different databases and measuring the accuracy of several different datasets. This study proposes a Bayesian Density Estimation-based model for detecting human sperm heads with four classification labels, namely, normal, tapered, pyriform, and small or amorphous. This model was applied to three kinds of datasets to detect the level of pixel density in images containing normal human sperm head samples. Experimental results and computational accuracy are also presented. As a method, this study labeled each human sperm head based on three shape descriptors using the formulas of Hu moment, Zernike moment, and Fourier descriptor. Each descriptor was also tested in the experiment. There was an increased accuracy that reached 90% after the model was applied to the three datasets. The Bayesian Density Estimation model could classify images containing human sperm head samples. The correct classification level was obtained when the human sperm head was detected by combining Bayesian + Hu moment with an accuracy rate of up to 90% which could detect normal human sperm heads. It is concluded that the proposed model can detect and classify images containing human sperm head objects. This model can increase accuracy, so it is very appropriate to be applied in the medical field

Local Constraint and Label Embedding Multi-layer Dictionary Learning for Sperm Head Classification

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-16
Author(s):  
Tongguang Ni ◽  
Yan Ding ◽  
Jing Xue ◽  
Kaijian Xia ◽  
Xiaoqing Gu ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Error Function ◽  
Human Sperm ◽  
Absolute Error ◽  
Sperm Head ◽  
Classification Model ◽  
Misclassification Error ◽  
Learning Capability ◽  
Local Constraint ◽  
Learning Framework

Morphological classification of human sperm heads is a key technology for diagnosing male infertility. Due to its sparse representation and learning capability, dictionary learning has shown remarkable performance in human sperm head classification. To promote the discriminability of the classification model, a novel local constraint and label embedding multi-layer dictionary learning model called LCLM-MDL is proposed in this study. Based on the multi-layer dictionary learning framework, two dictionaries are built on the basis of Laplacian regularized constraint and label embedding term in each layer, and the two dictionaries are approximated to each other as much as possible, so as to well exploit the nonlinear structure and discriminability features of the morphology of human sperm heads. In addition, to promote the robustness of the model, the asymmetric Huber loss is adopted in the last layer of LCLM-MDL, which approximates the misclassification error by using the absolute error function. Finally, the experimental results on HuSHeM dataset demonstrate the validity of the LCLM-MDL.

A motile human sperm head fixation method

Andrologia ◽  
2009 ◽  
Vol 25 (2) ◽  
pp. 67-70 ◽  
Author(s):  
M. C. Ou ◽  
H. T. Ng ◽  
B. N. Chiang ◽  
C. Y. Hong ◽  
C. T. Hsu
Keyword(s):  
Human Sperm ◽  
Sperm Head ◽  
Fixation Method ◽  
Head Fixation

Magnolol Protects Human Sperm Motility Against Lipid Peroxidation: A Sperm Head Fixation Method

1995 ◽  
Vol 34 (3) ◽  
pp. 151-156 ◽  
Author(s):  
M. H. Lin ◽  
H. T. Chao ◽  
C. Y. Hong
Keyword(s):  
Lipid Peroxidation ◽  
Sperm Motility ◽  
Human Sperm ◽  
Sperm Head ◽  
Fixation Method ◽  
Head Fixation
Sign in / Sign up

Export Citation Format

Share Document