Imperial College London
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MrAhmadSayasneh

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Honorary Clinical Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 3313 5131a.sayasneh

 
 
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Location

 

Institute of Reproductive and Developmental BiologyHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Khazendar:2015,
author = {Khazendar, S and Sayasneh, A and Al-Assam, H and Du, H and Kaijser, J and Ferrara, L and Timmerman, D and Jassim, S and Bourne, T},
journal = {Facts, Views and Vision in ObGyn},
pages = {7--15},
title = {Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator.},
url = {http://hdl.handle.net/10044/1/22115},
volume = {7},
year = {2015}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - INTRODUCTION: Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management. OBJECTIVES: In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant. MATERIALS AND METHODS: Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected. RESULTS: The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test). CONCLUSION: We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered.
AU  - Khazendar,S
AU  - Sayasneh,A
AU  - Al-Assam,H
AU  - Du,H
AU  - Kaijser,J
AU  - Ferrara,L
AU  - Timmerman,D
AU  - Jassim,S
AU  - Bourne,T
EP  - 15
PY  - 2015///
SN  - 2032-0418
SP  - 7
TI  - Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator.
T2  - Facts, Views and Vision in ObGyn
UR  - http://hdl.handle.net/10044/1/22115
VL  - 7
ER  -
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