Davila Garcia, M.L. orcid.org/0000-0001-6259-5781, Paredes Soto, D.A. and Mihaylova, L.S. (2018) A Bag of Features Based Approach for Classification of Motile Sperm Cells. In: 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). 10th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom-2017), 21-23 Jun 2017, Exeter, UK. IEEE ISBN 978-1-5386-3066-2
Abstract
The analysis of sperm morphology remains an essential process for diagnosis and treatment of male infertility. In this paper, a novel framework based on image processing is proposed to classify sperm cell images affected by noise due to their movement. This represents a challenge, articularly because the cells are not fixed or stained. The proposed framework is based on Speeded-Up Robust Features (SURF) combined with Bag of Features (BoF) models to quantise features computed by SURF. Support Vector Machines (SVMs) are used to classify the simplified feature vectors, extracted from sperm cell images, into normal, abnormal and noncell categories. The performance of this framework is compared to a similar model where the Histogram of Oriented Gradients (HOG) is used to extract features and SVMs is applied for their classification. The proposed framework allows to achieve classification results with an average accuracy of 90% with the SURF approach compared to 78% with the HOG approach.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. This is an author produced version of a paper subsequently published in 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 May 2017 10:39 |
Last Modified: | 15 Mar 2018 15:02 |
Published Version: | https://doi.org/ 10.1109/iThings-GreenCom-CPSCom-S... |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.21 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:116761 |