Gold-standard and improved framework for sperm head segmentation

Semen analysis is the first step in the evaluation of an infertile couple. Within this process, an accurate and objective morphological analysis becomes more critical as it is based on the correct detection and segmentation of human sperm components. In this paper, we present an improved two-stage framework for detection and segmentation of human sperm head characteristics (including acrosome and nucleus) that uses three different color spaces. The first stage detects regions of interest that define sperm heads, using k-means, then candidate heads are refined using mathematical morphology. In the second stage, we work on each region of interest to segment accurately the sperm head as well as nucleus and acrosome, using clustering and histogram statistical analysis techniques. Our proposal is also characterized by being fully automatic, where a user intervention is not required. Our experimental evaluation shows that our proposed method outperforms the state-of-the-art. This is supported by the results of different evaluation metrics. In addition, we propose a gold-standard built with the cooperation of a referent expert in the field, aiming to compare methods for detecting and segmenting sperm cells. Our results achieve notable improvement getting above 98% in the sperm head detection process at the expense of having significantly fewer false positives obtained by the state-of-the-art method. Our results also show an accurate head, acrosome and nucleus segmentation achieving over 80% overlapping against hand-segmented gold-standard. Our method achieves higher Dice coefficient, lower Hausdorff distance and less dispersion with respect to the results achieved by the state-of-the-art method.

[1]  M Morshedi,et al.  Intra- and inter-laboratory variability in the assessment of sperm morphology by strict criteria: impact of semen preparation, staining techniques and manual versus computerized analysis. , 1999, Human reproduction.

[2]  T. Kanade,et al.  Color information for region segmentation , 1980 .

[3]  B H Mayall,et al.  Quantification and classification of human sperm morphology by computer-assisted image analysis. , 1988, Fertility and sterility.

[4]  James C. Bezdek,et al.  A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain , 1992, IEEE Trans. Neural Networks.

[5]  J. Auger,et al.  WHO laboratory manual for the examination and processing of human semen , 2010 .

[6]  Jie Yao,et al.  A multi-population genetic algorithm for robust and fast ellipse detection , 2005, Pattern Analysis and Applications.

[7]  B. H. Erickson DEVELOPMENT AND RADIO-RESPONSE OF THE PRENATAL BOVINE OVARY , 1966 .

[8]  Matti Pietikäinen,et al.  Accurate color discrimination with classification based on feature distributions , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[9]  Kevin Coetzee,et al.  Repeatability and variance analysis on multiple computer-assisted (IVOS) sperm morphology readings. , 1999 .

[10]  Enrique Alegre,et al.  A combined and intelligent new segmentation method for boar semen based on thresholding and Watershed transform , 2009 .

[11]  G. Bartsch,et al.  Spermiometrics: objective and reproducible methods for evaluating sperm morphology. , 1982, European urology.

[12]  A Spira,et al.  Intra- and inter-individual variability in human sperm concentration, motility and vitality assessment during a workshop involving ten laboratories. , 2000, Human reproduction.

[13]  Umi Kalthum Ngah,et al.  Adaptive fuzzy moving K-means clustering algorithm for image segmentation , 2009, IEEE Transactions on Consumer Electronics.

[14]  Carlos Lopez-Molina,et al.  Interval-Valued Restricted Equivalence Functions Applied on Clustering Techniques , 2009, IFSA/EUSFLAT Conf..

[15]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[16]  C. Soler,et al.  Use of the Sperm-Class Analyser for objective assessment of human sperm morphology. , 2003, International journal of andrology.

[17]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[18]  Weiyuan Cui,et al.  Mother or nothing: the agony of infertility. , 2010, Bulletin of the World Health Organization.

[19]  Mohammad Hasan Moradi,et al.  Sperm Identification Using Elliptic Model and Tail Detection , 2005 .

[20]  Helge J. Ritter,et al.  Adaptive color segmentation-a comparison of neural and statistical methods , 1997, IEEE Trans. Neural Networks.

[21]  J. Jagoe,et al.  Morphometry of spermatozoa using semiautomatic image analysis. , 1986, Journal of clinical pathology.

[22]  Marcelino Hernández-Valencia,et al.  [Estimate of the variability in the evaluation of semen analysis]. , 2013, Ginecologia y obstetricia de Mexico.

[23]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[24]  Víctor González-Castro,et al.  Texture and moments-based classification of the acrosome integrity of boar spermatozoa images , 2012, Comput. Methods Programs Biomed..

[25]  Ricardo Gutierrez,et al.  A Computer Aided Tool for the Assessment of Human Sperm Morphology , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[26]  J. Auger Assessing human sperm morphology: top models, underdogs or biometrics? , 2010, Asian journal of andrology.

[27]  Koraljka Đurić,et al.  Sperm morphology assessment according to WHO and strict criteria: method comparison and intra-laboratory variability , 2009 .

[28]  Nicolai Petkov,et al.  Estimation of Boar Sperm Status Using Intracellular Density Distribution in Grey Level Images , 2009, Similarity-Based Clustering.

[29]  D. Bain,et al.  Accuracy and precision of the CellForm-Human automated sperm morphometry instrument. , 1992, Fertility and sterility.

[30]  F A Lacquet,et al.  Slide preparation and staining procedures for reliable results using computerized morphology. , 1996, Archives of andrology.

[31]  E. L. Lewis,et al.  Morphometric analysis of spermatozoa in the assessment of human male fertility. , 1986, Journal of andrology.

[32]  M. Mikaeili,et al.  Fully automatic identification and discrimination of sperm's parts in microscopic images of stained human semen smear , 2012 .

[33]  A Leung,et al.  Computer-assisted assessment of human sperm morphology: comparison with visual assessment. , 1991, Fertility and sterility.

[34]  C. Gravance,et al.  Standardization of specimen preparation, staining, and sampling methods improves automated sperm-head morphometry analysis. , 1993, Fertility and sterility.

[35]  W. Yi,et al.  Parameterized characterization of elliptic sperm heads using Fourier representation and wavelet transform , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[36]  Leonidas J. Guibas,et al.  Discrete Geometric Shapes: Matching, Interpolation, and Approximation , 2000, Handbook of Computational Geometry.

[37]  C. Soler,et al.  Morphometric analysis of human sperm morphology. , 1994, International journal of andrology.

[38]  C. Lombard,et al.  A new computerized method of reading sperm morphology (strict criteria) is as efficient as technician reading. , 1993, Fertility and sterility.

[39]  J. Słowikowska-Hilczer,et al.  Semen analysis standardization: is there any problem in Polish laboratories? , 2013, Asian journal of andrology.

[40]  V. Shanthi,et al.  Spermatozoa Segmentation and Morphological Parameter Analysis Based Detection of Teratozoospermia , 2010 .

[41]  Kwang Suk Park,et al.  Segmentation of sperms using the strategic hough transform , 1997, Annals of Biomedical Engineering.

[42]  Mehmet Celenk,et al.  A color clustering technique for image segmentation , 1990, Comput. Vis. Graph. Image Process..

[43]  Anil K. Jain,et al.  Face Detection in Color Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  Ahmad Bijar,et al.  Sperm's tail identification and discrimination in microscopic images of stained human semen smear , 2011, 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA).

[45]  M Freund,et al.  Standards for the rating of human sperm morphology. A cooperative study. , 1966, International journal of fertility.

[46]  Richard Friedman,et al.  The prevalence and predictability of depression in infertile women. , 1992, Fertility and sterility.

[47]  Georgios Tziritas,et al.  Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis , 1999, IEEE Trans. Multim..