Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear

Affiliation(s)

Department of Biomedical Engineering, Shahed University, Tehran, Iran.

Center of Operations Research University Institute Miguel Hernández University of Elche, Spain.

Department of Biomedical Engineering, Shahed University, Tehran, Iran.

Center of Operations Research University Institute Miguel Hernández University of Elche, Spain.

ABSTRACT

In the last years, digital image processing and analysis are used for computer assisted evaluation of semen quality with therapeutic goals or to estimate its fertility by means of spermatozoid motility and morphology. Sperm morphology is assessed routinely as part of standard laboratory analysis in the diagnosis of human male infertility. Nowadays assessments of sperm morphology are mostly done based on subjective criteria. In order to avoid subjectivity, numerous studies that incorporate image analysis techniques in the assessment of sperm morphology have been proposed. The primary step of all these methods is segmentation of sperm’s parts. In this paper, we have proposed a new method for segmentation of sperm’s Acrosome, Nucleus, Mid-piece and identification of sperm’s tail through some points which are placed on the sperm’s tail, accurately. These estimated points could be used to verify the morphological characteristics of sperm’s tail such as length, shape and etc. At first, sperm’s Acrosome, Nucleus and Mid-piece are segmented through a method based on a Bayesian classifier which utilizes the entropy based expectation–maximization (EM) algorithm and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the*apriori* probability of each class. Then, a pixel at the end of sperm’s Mid-piece, is selected as an initial point. To find other pixels which are placed on the sperm’s tail, structural similarity index (SSIM) is used in an iterative scheme. In order to stop the algorithm automatically at the end of sperm’s tail, local entropy is estimated and used as a feature to determine if a point is located on the sperm’s tail or not. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the Accuracy, Sensitivity and Specificity were calculated.

In the last years, digital image processing and analysis are used for computer assisted evaluation of semen quality with therapeutic goals or to estimate its fertility by means of spermatozoid motility and morphology. Sperm morphology is assessed routinely as part of standard laboratory analysis in the diagnosis of human male infertility. Nowadays assessments of sperm morphology are mostly done based on subjective criteria. In order to avoid subjectivity, numerous studies that incorporate image analysis techniques in the assessment of sperm morphology have been proposed. The primary step of all these methods is segmentation of sperm’s parts. In this paper, we have proposed a new method for segmentation of sperm’s Acrosome, Nucleus, Mid-piece and identification of sperm’s tail through some points which are placed on the sperm’s tail, accurately. These estimated points could be used to verify the morphological characteristics of sperm’s tail such as length, shape and etc. At first, sperm’s Acrosome, Nucleus and Mid-piece are segmented through a method based on a Bayesian classifier which utilizes the entropy based expectation–maximization (EM) algorithm and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the

Cite this paper

Bijar, A. , Benavent, A. , Mikaeili, M. and khayati, R. (2012) Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear.*Journal of Biomedical Science and Engineering*, **5**, 384-395. doi: 10.4236/jbise.2012.57049.

Bijar, A. , Benavent, A. , Mikaeili, M. and khayati, R. (2012) Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear.

References

[1] Domar, A.D., Broome, A., Zuttermeister P.C., Seibel, M. and Friedman, R. (1992) The prevalence and predictability of depression in infertile women. Fertil. Steril, 58(6), 1158-1163.

[2] World Health Organization (1999) WHO laboratory manual for examination of human semen and sperm cervical mucus interaction, 4 Ed. Cambridge: Cambridge university press.

[3] Katz, D.F., Overstreet, J.W., Samuels, S. J., Niswander, P.W., Bloom, T. D., and Lewis, E.L. (1985) Morphometric analysis of spermatozoa in the assessment of human male fertility. Journal of Andrology, 7, 203-210.

[4] Sánchez L., Petkov, N., and Alegre, E. (2006) Statistical approach to boar semen evaluation using intracellular intensity distribution of head images. Cellular and Molecular Biology, 52( 6), 38-43. doi: 10.1170/T736

[5] Gravance, C.G., Garner, D.L., Pitt, C., Vishwanath, R., Sax-Gravance, S.K., and Casey, P.J. (1999) Replicate and technician variation associated with computer aided bull sperm head morphometry analysis (ASMA). International Journal of Andrology, 22, 77-82.

[6] Hirai, M., Boersma, A., Hoeflich, A., Wolf, E., Foll, J., Aumuller, T., and Braun, J. (2001) Objectively measured sperm motility and sperm head morphometry in boars (Sus scrofa): relation to fertility and seminal plasma growth factors. J. Androl., 22, 104-110.

[7] Wijchman, J., Wolf, B.D., Graafe, R., and Arts, E. (2001) Variation in semen parameters derived from computer-aided semen analysis, within donors and between donors. J. Androl., 22, 773-780.

[8] Quintero-Moreno, A., Rigaub, T., and Rodrguez-Gil, J.E. (2001) Regression analyses and motile sperm subpopulation structure study as improving tools in boar semen quality analysis. Theriogenology, 61, 673-690. doi:10.1016/S0093-691X(03)00248-6

[9] Rijsselaere, T., Soom, A.V., Hoflack, G., Maes, D., and de Kruif, A. (2004) Automated sperm morphometry and morphology analysis of canine semen by the Hamilton-Thorne analyser. Theriogenology, 62, 1292-1306. doi:10.1016/j.theriogenology.2004.01.005

[10] Verstegen, J., Iguer-Ouada, M., and Onclin, K. (2001) Computer assisted semen analyzers in andrology research and vete-rinary practice. Theriogenology, 57, 149-179.

[11] Beletti, M., Costa, L., and Viana, M. (2005) A comparison of morphometric characteristics of sperm from fertile bos taurus and bos indicus bulls in Brazil. Animal Re-production Science, 85, 105-116. doi:10.1016/j.anireprosci.2004.04.019

[12] Garrett, C., and Baker, H. (1995) A new fully automated system for the morphometric analysis of human sperm heads. Fertil. Steril., 63, 1306-1317.

[13] Linneberg, C., Salamon, P., Svarer, C., and Hansen, L. (1994) Towards semen quality assessment using neural networks. In: Proc. IEEE Neural Networks for Signal Processing IV., 509-517. doi:10.1109/NNSP.1994.366015

[14] Ostermeier, G., Sargeant, G., Yandell, T., and Parrish, J. (2001) Mea-surement of bovine sperm nuclear shape using Fourier harmonic amplitudes. J. Androl., 22, 584-594.

[15] Sánchez, L., Petkov, N., and Alegre, E. (2005) Statistical approach to boar semen head classification based on intracellular intensity distribution. in: A. Gagalowicz, W. Philips (Eds.) Proceedings of the International Conference on Computer Analysis of Images and Patterns, CAIP 2005, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 3691, 88-95. doi:10.1007/11556121_12

[16] Otsu, N. (1979) A thre-shold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 9, 62-66. doi:10.1109/TSMC.1979.4310076

[17] Biehl, W.M., Pasma, P., Pijl, M., Sánchez, L., and Petkov, N. (2006) Classification of boar sperm head images using learning vector quantization. in: M. Verleysen (Ed.), Proceedings of the European Symposium on Artificial Neural Networks (ESANN), Brugge, April 26-28, 2006, d-side, Evere, Belgium., 545-550.

[18] Alegre, E., Biehl, M., Petkov, N., and Sánchez, L. (2008) Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ. Computers in Biology and Medicine, 38, 461-468. doi:10.1016/j.compbiomed.2008.01.005

[19] Nowshiravan Rahatabad, F., Moradi, M.H., and Nafisi, V.R. (2005) A Multi Steps Algorithm for Sperm Segmentation in Mi-croscopic Image. Proceedings of the World Academy of Science, Engineering and Technology, 12, 43-45.

[20] Nafisi, V.R., Moradi, M.H., and Nasr-Esfahani, M.H. (2005) Sperm Identification Using Elliptic Model and Tail Detection, Proceedings of the World Academy of Science, Engineering and Technology, 6, 205-208.

[21] Park, K., Yi, W., and Paick, J. (1997) Segmentation of sperms using the strategic Hough trans-form. Annals of Biomedical Engineering, 25, 294-302. doi:10.1007/BF02648044

[22] Carrillo, H., Villarreal, J., Sotaquira, M., Goelkel, M., and Gutierrez, R. (2007) A Computer Aided Tool for the Assessment of Humnan Sperm Morphology. Proceedings of the 7th IEEE Inter-national Conference on Bioinformatics and Bioengineer-ing (BIBE), 1152-1157. doi:10.1109/BIBE.2007.4375706

[23] Carrillo, H., Vil-larreal, J., Sotaquira, M., Goelkel, M., and Gutierrez, R. (2005) Spermatozoon Segmentation Towards an Objective Analysis of Human Sperm Morphology. Proceedings of the 5th International Symposium on image and Signal Processing and Analysis, 522-527. doi:10.1109/ISPA.2007.4383748

[24] Abbiramy V.S., and Shanthi, V. (2010) Spermatozoa Segmentation and Morphological Parameter Analysis Based Detection of Teratozoospermia. International Journal of Computer Applications, 3(7), 19-23. doi:10.5120/743-1050

[25] Rajan, J., Kannan, K., and Kaimal, M.R. (2008) An Improved Hybrid Model for Molecular Image Denoising. Journal of Mathematical Imaging and Vision, 31, 73-79. doi:10.1007/s10851-008-0067-4

[26] Pirzadeh, H. (1999) Computational geometry with the rotating calipers. Master thesis, School of Computer Science, McGill Uni-versity, Montreal, Quebec, Canada.

[27] Toussaint, G.T. (1983) Solving geometric problems with the rotating ca-lipers. In Proceedings of IEEE MELECON83, Athens, Greece. doi:10.1.1.40.2140

[28] Benavent, A.P., Ruiz, F.E., and Sáez, J.M. (2009) Learning Gaussian Mixture Models With Entropy-Based Criteria. IEEE Transactions on Neural Networks, 20(11), 1756-1771. doi: 10.1109/TNN.2009.2030190

[29] Bijar, A., Mohamad Khanloo, M., Benavent, A.P., and Khayati, R. (2011) Segmentation of MS lesions using entropy-based EM al-gorithm and Markov random fields. Journal of Biomedical Science and Engineering (JBISE), 4 (8), 552-561. doi: 10.4236/jbise.2011.48071

[30] Wang, Z., Bovik, A.C., Sheikh, H.R., and Simoncelli, E.P. (2004) Image quality assessment: From error measurement to structural simi-larity. IEEE Trans. Image Processing., 13(1), 1-14. doi: 10.1109/TIP.2003.819861

[31] Rényi, A. (1961) On measures of entropy and information., Proc. 4th Berkeley Sympos. Math. Statist. Probab. Univ. California Press, Berkeley. MR0132570, 547-561.

[32] Bijar, A., and Mikaeili, M. (2011) Sperm’s tail identification and dis-crimination in microscopic images of stained human semen smear. Proceeding of the 7th International Symposium on Image and Signal Processing and Analysis (ISPA), Croatia, 709-714.

[33] You, Y.L., Kaveh, M. (2000) Fourth-order partial differential equations for noise re-moval. IEEE Trans. Image Process. 9, 1723-1730. doi:10.1109/83.869184

[34] Perona, P., Malik, J. (1988) Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intel. 12, 629-639. doi: 10.1109/34.56205

[35] Hamza, A.B., Escamilla, P.L., Aroza, J.M., Roldan, R. (1999) Removing noise and preserving details with relaxed median filters. J. Math. Imaging Vis. 11, 161-177. doi:10.1023/A:1008395514426

[36] Hamza, A.B., Krim, H. (2001)Image denoising: a nonlinear robust statistical approach. IEEE Trans. Signal Process. 49(12), 3045-3054. doi: 10.1109/78.969512

[37] Andrew, A. (1997) Another efficient algorithm for convex hulls in two dimensions. In Info. Proc. Letters 9. doi:10.1016/0020-0190(79)90072-3

[38] Brabec, S., Annen, T., and Seidel, H.P. (2002) Practical Shadow Mapping. Journal of Graphics, GPU, and Game Tools, 7(4), 9-18.

[39] Duda, R.O., Hart, P.E., and Stork, D.G. (2001) Pattern Classification, second ed, Wiley, New York.

[40] Bernardo, J.M., and Smith, A.F.M. (1994) Bayesian Theory. Wiley, New York.

[1] Domar, A.D., Broome, A., Zuttermeister P.C., Seibel, M. and Friedman, R. (1992) The prevalence and predictability of depression in infertile women. Fertil. Steril, 58(6), 1158-1163.

[2] World Health Organization (1999) WHO laboratory manual for examination of human semen and sperm cervical mucus interaction, 4 Ed. Cambridge: Cambridge university press.

[3] Katz, D.F., Overstreet, J.W., Samuels, S. J., Niswander, P.W., Bloom, T. D., and Lewis, E.L. (1985) Morphometric analysis of spermatozoa in the assessment of human male fertility. Journal of Andrology, 7, 203-210.

[4] Sánchez L., Petkov, N., and Alegre, E. (2006) Statistical approach to boar semen evaluation using intracellular intensity distribution of head images. Cellular and Molecular Biology, 52( 6), 38-43. doi: 10.1170/T736

[5] Gravance, C.G., Garner, D.L., Pitt, C., Vishwanath, R., Sax-Gravance, S.K., and Casey, P.J. (1999) Replicate and technician variation associated with computer aided bull sperm head morphometry analysis (ASMA). International Journal of Andrology, 22, 77-82.

[6] Hirai, M., Boersma, A., Hoeflich, A., Wolf, E., Foll, J., Aumuller, T., and Braun, J. (2001) Objectively measured sperm motility and sperm head morphometry in boars (Sus scrofa): relation to fertility and seminal plasma growth factors. J. Androl., 22, 104-110.

[7] Wijchman, J., Wolf, B.D., Graafe, R., and Arts, E. (2001) Variation in semen parameters derived from computer-aided semen analysis, within donors and between donors. J. Androl., 22, 773-780.

[8] Quintero-Moreno, A., Rigaub, T., and Rodrguez-Gil, J.E. (2001) Regression analyses and motile sperm subpopulation structure study as improving tools in boar semen quality analysis. Theriogenology, 61, 673-690. doi:10.1016/S0093-691X(03)00248-6

[9] Rijsselaere, T., Soom, A.V., Hoflack, G., Maes, D., and de Kruif, A. (2004) Automated sperm morphometry and morphology analysis of canine semen by the Hamilton-Thorne analyser. Theriogenology, 62, 1292-1306. doi:10.1016/j.theriogenology.2004.01.005

[10] Verstegen, J., Iguer-Ouada, M., and Onclin, K. (2001) Computer assisted semen analyzers in andrology research and vete-rinary practice. Theriogenology, 57, 149-179.

[11] Beletti, M., Costa, L., and Viana, M. (2005) A comparison of morphometric characteristics of sperm from fertile bos taurus and bos indicus bulls in Brazil. Animal Re-production Science, 85, 105-116. doi:10.1016/j.anireprosci.2004.04.019

[12] Garrett, C., and Baker, H. (1995) A new fully automated system for the morphometric analysis of human sperm heads. Fertil. Steril., 63, 1306-1317.

[13] Linneberg, C., Salamon, P., Svarer, C., and Hansen, L. (1994) Towards semen quality assessment using neural networks. In: Proc. IEEE Neural Networks for Signal Processing IV., 509-517. doi:10.1109/NNSP.1994.366015

[14] Ostermeier, G., Sargeant, G., Yandell, T., and Parrish, J. (2001) Mea-surement of bovine sperm nuclear shape using Fourier harmonic amplitudes. J. Androl., 22, 584-594.

[15] Sánchez, L., Petkov, N., and Alegre, E. (2005) Statistical approach to boar semen head classification based on intracellular intensity distribution. in: A. Gagalowicz, W. Philips (Eds.) Proceedings of the International Conference on Computer Analysis of Images and Patterns, CAIP 2005, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 3691, 88-95. doi:10.1007/11556121_12

[16] Otsu, N. (1979) A thre-shold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 9, 62-66. doi:10.1109/TSMC.1979.4310076

[17] Biehl, W.M., Pasma, P., Pijl, M., Sánchez, L., and Petkov, N. (2006) Classification of boar sperm head images using learning vector quantization. in: M. Verleysen (Ed.), Proceedings of the European Symposium on Artificial Neural Networks (ESANN), Brugge, April 26-28, 2006, d-side, Evere, Belgium., 545-550.

[18] Alegre, E., Biehl, M., Petkov, N., and Sánchez, L. (2008) Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ. Computers in Biology and Medicine, 38, 461-468. doi:10.1016/j.compbiomed.2008.01.005

[19] Nowshiravan Rahatabad, F., Moradi, M.H., and Nafisi, V.R. (2005) A Multi Steps Algorithm for Sperm Segmentation in Mi-croscopic Image. Proceedings of the World Academy of Science, Engineering and Technology, 12, 43-45.

[20] Nafisi, V.R., Moradi, M.H., and Nasr-Esfahani, M.H. (2005) Sperm Identification Using Elliptic Model and Tail Detection, Proceedings of the World Academy of Science, Engineering and Technology, 6, 205-208.

[21] Park, K., Yi, W., and Paick, J. (1997) Segmentation of sperms using the strategic Hough trans-form. Annals of Biomedical Engineering, 25, 294-302. doi:10.1007/BF02648044

[22] Carrillo, H., Villarreal, J., Sotaquira, M., Goelkel, M., and Gutierrez, R. (2007) A Computer Aided Tool for the Assessment of Humnan Sperm Morphology. Proceedings of the 7th IEEE Inter-national Conference on Bioinformatics and Bioengineer-ing (BIBE), 1152-1157. doi:10.1109/BIBE.2007.4375706

[23] Carrillo, H., Vil-larreal, J., Sotaquira, M., Goelkel, M., and Gutierrez, R. (2005) Spermatozoon Segmentation Towards an Objective Analysis of Human Sperm Morphology. Proceedings of the 5th International Symposium on image and Signal Processing and Analysis, 522-527. doi:10.1109/ISPA.2007.4383748

[24] Abbiramy V.S., and Shanthi, V. (2010) Spermatozoa Segmentation and Morphological Parameter Analysis Based Detection of Teratozoospermia. International Journal of Computer Applications, 3(7), 19-23. doi:10.5120/743-1050

[25] Rajan, J., Kannan, K., and Kaimal, M.R. (2008) An Improved Hybrid Model for Molecular Image Denoising. Journal of Mathematical Imaging and Vision, 31, 73-79. doi:10.1007/s10851-008-0067-4

[26] Pirzadeh, H. (1999) Computational geometry with the rotating calipers. Master thesis, School of Computer Science, McGill Uni-versity, Montreal, Quebec, Canada.

[27] Toussaint, G.T. (1983) Solving geometric problems with the rotating ca-lipers. In Proceedings of IEEE MELECON83, Athens, Greece. doi:10.1.1.40.2140

[28] Benavent, A.P., Ruiz, F.E., and Sáez, J.M. (2009) Learning Gaussian Mixture Models With Entropy-Based Criteria. IEEE Transactions on Neural Networks, 20(11), 1756-1771. doi: 10.1109/TNN.2009.2030190

[29] Bijar, A., Mohamad Khanloo, M., Benavent, A.P., and Khayati, R. (2011) Segmentation of MS lesions using entropy-based EM al-gorithm and Markov random fields. Journal of Biomedical Science and Engineering (JBISE), 4 (8), 552-561. doi: 10.4236/jbise.2011.48071

[30] Wang, Z., Bovik, A.C., Sheikh, H.R., and Simoncelli, E.P. (2004) Image quality assessment: From error measurement to structural simi-larity. IEEE Trans. Image Processing., 13(1), 1-14. doi: 10.1109/TIP.2003.819861

[31] Rényi, A. (1961) On measures of entropy and information., Proc. 4th Berkeley Sympos. Math. Statist. Probab. Univ. California Press, Berkeley. MR0132570, 547-561.

[32] Bijar, A., and Mikaeili, M. (2011) Sperm’s tail identification and dis-crimination in microscopic images of stained human semen smear. Proceeding of the 7th International Symposium on Image and Signal Processing and Analysis (ISPA), Croatia, 709-714.

[33] You, Y.L., Kaveh, M. (2000) Fourth-order partial differential equations for noise re-moval. IEEE Trans. Image Process. 9, 1723-1730. doi:10.1109/83.869184

[34] Perona, P., Malik, J. (1988) Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intel. 12, 629-639. doi: 10.1109/34.56205

[35] Hamza, A.B., Escamilla, P.L., Aroza, J.M., Roldan, R. (1999) Removing noise and preserving details with relaxed median filters. J. Math. Imaging Vis. 11, 161-177. doi:10.1023/A:1008395514426

[36] Hamza, A.B., Krim, H. (2001)Image denoising: a nonlinear robust statistical approach. IEEE Trans. Signal Process. 49(12), 3045-3054. doi: 10.1109/78.969512

[37] Andrew, A. (1997) Another efficient algorithm for convex hulls in two dimensions. In Info. Proc. Letters 9. doi:10.1016/0020-0190(79)90072-3

[38] Brabec, S., Annen, T., and Seidel, H.P. (2002) Practical Shadow Mapping. Journal of Graphics, GPU, and Game Tools, 7(4), 9-18.

[39] Duda, R.O., Hart, P.E., and Stork, D.G. (2001) Pattern Classification, second ed, Wiley, New York.

[40] Bernardo, J.M., and Smith, A.F.M. (1994) Bayesian Theory. Wiley, New York.