Related Experiment Video
Updated: Apr 14, 2026
A Coregistered Ultrasound and Photoacoustic Imaging Protocol for the Transvaginal Imaging of Ovarian Lesions
Published on: March 3, 2023
Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector
S Khazendar1, A Sayasneh2, H Al-Assam1
1Department of Applied Computing, University of Buckingham, Buckingham, MK18 1EG, U.K.
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.
