Sperm Identification Using Elliptic Model and Tail Detection
The conventional assessment of human semen is a
highly subjective assessment, with considerable intra- and interlaboratory
variability. Computer-Assisted Sperm Analysis (CASA)
systems provide a rapid and automated assessment of the sperm
characteristics, together with improved standardization and quality
control. However, the outcome of CASA systems is sensitive to the
method of experimentation. While conventional CASA systems use
digital microscopes with phase-contrast accessories, producing
higher contrast images, we have used raw semen samples (no
staining materials) and a regular light microscope, with a digital
camera directly attached to its eyepiece, to insure cost benefits and
simple assembling of the system. However, since the accurate finding
of sperms in the semen image is the first step in the examination and
analysis of the semen, any error in this step can affect the outcome of
the analysis. This article introduces and explains an algorithm for
finding sperms in low contrast images: First, an image enhancement
algorithm is applied to remove extra particles from the image. Then,
the foreground particles (including sperms and round cells) are
segmented form the background. Finally, based on certain features
and criteria, sperms are separated from other cells.
[1] Domar AD, Broome A, Zuttermeister PC, Seibel M, Friedman R (1992),
The prevalence and predictability of depression in infertile women,
Fertil. & Steril, 58, 1158-1163.
[2] Acosta AA, Kruger TF (1996), Human spermatozoa in assisted
reproduction, 2nd edition, The Partheon Publishing Group, Chapter 6,
53-71.
[3] WHO (1999), Laboratory Manual for the Examination of Human Semen
and Sperm-Cervical Mucus Interaction, The press Syndicate of the
University of Cambridge, Cambridge, United Kingdom.
[4] ESHRE Andrology Special Interest Group (1998), Guidelines on the
Application of CASA Technology in the Analysis of Spermatozoa,
Human Reproduction, Vol. 13, No. 1, pp. 142-145.
[5] Wijchman JG, de Wolf BT, Jager S (1995), Evaluation of a computeraided
semen analysis system with sperm tail detection, Human
Reproduction, 10, 2090-2095.
[6] Zinaman MJ, et al (1996), Evaluation of computer-assisted semen
analysis (CASA) with IDENT stain to determine sperm concentration,
Journal of Andrology, 17, 288-292.
[7] Nafisi VR, Moradi MH, Nasr-esfahani MH (2005), A Template
Matching Algorithm for Sperm Tracking and Classification, accepted in
Physiological Measurement.
[8] Pitas I. (2000), Digital image processing algorithms and applications,
published by John Wiley & Sons, New York, USA, chapter 7, 361-372.
[9] Teifoory N, Moradi MH, Nafisi VR (2002), A new method for sperm
segmentation in microscopic image, proceeding of 11th Iranian
biomedical engineering conference, Amirkabir University of
Technology, Tehran, Iran.
[1] Domar AD, Broome A, Zuttermeister PC, Seibel M, Friedman R (1992),
The prevalence and predictability of depression in infertile women,
Fertil. & Steril, 58, 1158-1163.
[2] Acosta AA, Kruger TF (1996), Human spermatozoa in assisted
reproduction, 2nd edition, The Partheon Publishing Group, Chapter 6,
53-71.
[3] WHO (1999), Laboratory Manual for the Examination of Human Semen
and Sperm-Cervical Mucus Interaction, The press Syndicate of the
University of Cambridge, Cambridge, United Kingdom.
[4] ESHRE Andrology Special Interest Group (1998), Guidelines on the
Application of CASA Technology in the Analysis of Spermatozoa,
Human Reproduction, Vol. 13, No. 1, pp. 142-145.
[5] Wijchman JG, de Wolf BT, Jager S (1995), Evaluation of a computeraided
semen analysis system with sperm tail detection, Human
Reproduction, 10, 2090-2095.
[6] Zinaman MJ, et al (1996), Evaluation of computer-assisted semen
analysis (CASA) with IDENT stain to determine sperm concentration,
Journal of Andrology, 17, 288-292.
[7] Nafisi VR, Moradi MH, Nasr-esfahani MH (2005), A Template
Matching Algorithm for Sperm Tracking and Classification, accepted in
Physiological Measurement.
[8] Pitas I. (2000), Digital image processing algorithms and applications,
published by John Wiley & Sons, New York, USA, chapter 7, 361-372.
[9] Teifoory N, Moradi MH, Nafisi VR (2002), A new method for sperm
segmentation in microscopic image, proceeding of 11th Iranian
biomedical engineering conference, Amirkabir University of
Technology, Tehran, Iran.
@article{"International Journal of Medical, Medicine and Health Sciences:64895", author = "Vahid Reza Nafisi and Mohammad Hasan Moradi and Mohammad Hosain Nasr-Esfahani", title = "Sperm Identification Using Elliptic Model and Tail Detection", abstract = "The conventional assessment of human semen is a
highly subjective assessment, with considerable intra- and interlaboratory
variability. Computer-Assisted Sperm Analysis (CASA)
systems provide a rapid and automated assessment of the sperm
characteristics, together with improved standardization and quality
control. However, the outcome of CASA systems is sensitive to the
method of experimentation. While conventional CASA systems use
digital microscopes with phase-contrast accessories, producing
higher contrast images, we have used raw semen samples (no
staining materials) and a regular light microscope, with a digital
camera directly attached to its eyepiece, to insure cost benefits and
simple assembling of the system. However, since the accurate finding
of sperms in the semen image is the first step in the examination and
analysis of the semen, any error in this step can affect the outcome of
the analysis. This article introduces and explains an algorithm for
finding sperms in low contrast images: First, an image enhancement
algorithm is applied to remove extra particles from the image. Then,
the foreground particles (including sperms and round cells) are
segmented form the background. Finally, based on certain features
and criteria, sperms are separated from other cells.", keywords = "Computer-Assisted Sperm Analysis (CASA), Sperm
identification, Tail detection, Elliptic shape model.", volume = "1", number = "6", pages = "407-4", }