Journal of Biomedical Science and Engineering

Journal of Biomedical Science and Engineering

ISSN Print: 1937-6871
ISSN Online: 1937-688X
www.scirp.org/journal/jbise
E-mail: jbise@scirp.org
"Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear"
written by Ahmad Bijar, Antonio Peñalver Benavent, Mohammad Mikaeili, Rasoul khayati,
published by Journal of Biomedical Science and Engineering, Vol.5 No.7, 2012
has been cited by the following article(s):
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[3] Robotic Manipulation of Sperm as a Deformable Linear Object
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[5] Automated rare sperm identification from low-magnification microscopy images of dissociated microsurgical testicular sperm extraction samples using deep learning
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[8] Deep Learning-based Automated Rare Sperm Identification from Testes Biopsies
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[9] Multi-model CNN fusion for sperm morphology analysis
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[10] Automatic identification from noisy microscopic images
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[11] Automatic Assessment of Human Sperm Images with Capsule Networks
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[12] Advances in sperm analysis: techniques, discoveries and applications
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[13] Robotic Cell Manipulation for Intracytoplasmic Sperm Injection (ICSI)
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[14] Повышение эффективности вспомогательных репродуктивных технологий с помощью искусственного интеллекта и машинного обучения на …
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[15] A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods
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[16] Supervised learning for semantic segmentation of human spermatozoa
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[17] Classification of Human Sperm Head in Microscopic Images Using Twin Support Vector Machine and Neural Network
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[18] Computer-Assisted Analysis of Human Semen Concentration and Motility
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[19] Automated sperm morphology analysis approach using a directional masking technique
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[20] A novel deep learning method for automatic assessment of human sperm images
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[21] Genetic Neural Architecture Search for automatic assessment of human sperm images
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[22] Smartphone based sperm counting-an alternative way to the visual assessment technique in sperm concentration analysis
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[23] Robotic Manipulation and Selection of Single Sperm for In Vitro Fertilization
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[24] Automated Morphology Detection from Human Sperm Images
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[25] Identification Male Fertility Through Abnormalities Sperm Based Morphology (Teratospermia) using Invariant Moment Method
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[26] Automated Non-Invasive Measurement of Single Sperm's Motility and Morphology
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[27] A New Method for Detecting Sperms in Microscopy Images: Combination of Zernike Moments and Spatial Processing
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[28] Use of classifiers and recursive feature elimination to assess boar sperm viability
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[29] The Effects of the Modified Overlapping Group Shrinkage Technique on the Sperm Segmentation in the Stained Images
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[30] An automatic system for spermiogram analysis based on image processing techniques and support vector machines
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[31] Convolutional neural networks for segmentation and object detection of human semen
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[32] A New Region-Based Adaptive Thresholding For Sperm Motility Segmentation
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[33] Automatic detection and segmentation of sperm head, acrosome and nucleus in microscopic images of human semen smears
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[34] An automated system for investigating sperm orientation in fluid flow
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[35] A NEW REGION-BASED ADAPTIVE THRESHOLDING FOR SPERM MOTILITY SEGMENTATION.
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[36] Use of Support Vector Machines and Neural Networks to Assess Boar Sperm Viability
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[37] Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization
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[38] An efficient method for automatic morphological abnormality detection from human sperm images
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[39] Sperm Cells Segmentation in Micrographic Images Through Lambertian Reflectance Model
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[40] Segmentation and classification of human sperm heads towards morphological sperm analysis
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[41] Gold-standard and improved framework for sperm head segmentation
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[42] Automatic sperms counting using adaptive local threshold and ellipse detection
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[43] Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ
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[44] Assessment of acrosome state in boar spermatozoa heads using< i> n-contours descriptor and RLVQ
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[45] Automated evaluation of gamete and embryo quality for assisted reproduction
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[46] Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ
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[47] Automation Techniques and Systems for ICSI
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[48] Automatic Identification of Human Sperms from Noisy Microscopic Images
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