Texture and moments-based classification of the acrosome integrity of boar spermatozoa images

Authors:
Enrique Alegre;VíCtor GonzáLez-Castro;RocíO Alaiz-RodríGuez;MaríA Teresa GarcíA-OrdáS
Affiliations:
Department of Electrical, Systems and Automatic Engineerings, University of León, 24071 León, Spain;Department of Electrical, Systems and Automatic Engineerings, University of León, 24071 León, Spain;Department of Electrical, Systems and Automatic Engineerings, University of León, 24071 León, Spain;Department of Electrical, Systems and Automatic Engineerings, University of León, 24071 León, Spain
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

Citing 14
Cited 3

The Case against Accuracy Estimation for Comparing Induction Algorithms

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Texture classification using wavelet transform

Pattern Recognition Letters
A Comparative Study of Zernike Moments

WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Digital Image Processing (3rd Edition)

Digital Image Processing (3rd Edition)
A texture approach to leukocyte recognition

Real-Time Imaging - Special issue on imaging in bioinformatics: Part III
A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography

Computers in Biology and Medicine
Automatic recognition and annotation of gene expression patterns of fly embryos

Bioinformatics
Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ

Computers in Biology and Medicine
Selection of human embryos for transfer by Bayesian classifiers

Computers in Biology and Medicine
Quantifying the proportion of damaged sperm cells based on image analysis and neural networks

SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments

Computers in Biology and Medicine
Statistical approach to boar semen head classification based on intracellular intensity distribution

CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Classification of boar spermatozoid head images using a model intracellular density distribution

CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications

IEEE Transactions on Information Technology in Biomedicine

Vitality assessment of boar sperm using an adaptive LBP based on oriented deviation

ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ

Computer Methods and Programs in Biomedicine
Preparation of 2D sequences of corneal images for 3D model building

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

The automated assessment of the sperm quality is an important challenge in the veterinary field. In this paper, we explore how to describe the acrosomes of boar spermatozoa using image analysis so that they can be automatically categorized as intact or damaged. Our proposal aims at characterizing the acrosomes by means of texture features. The texture is described using first order statistics and features derived from the co-occurrence matrix of the image, both computed from the original image and from the coefficients yielded by the Discrete Wavelet Transform. Texture descriptors are evaluated and compared with moments-based descriptors in terms of the classification accuracy they provide. Experimental results with a Multilayer Perceptron and the k-Nearest Neighbours classifiers show that texture descriptors outperform moment-based descriptors, reaching an accuracy of 94.93%, which makes this approach very attractive for the veterinarian community.