Author(s)

Ahmad Bijar, Antonio Peñalver Benavent, Mohammad Mikaeili, Rasoul khayati

Center of Operations Research University Institute Miguel Hernández University of Elche, Spain.
Department of Biomedical Engineering, Shahed University, Tehran, Iran.

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.

Sperm; Segmentation; Rotating Calipers; Bayesian Classification; Entropy Based EM Algorithm; Structural Similarity Index (SSIM); Entropy

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.