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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.

Khazendar S, Sayasneh A, Al-Assam H, Du H, Kaijser J, Ferrara L, Timmerman D, Jassim S, Bourne T - Facts Views Vis Obgyn (2015)

Bottom Line: Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image.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).The accuracy improves if texture related LBP features extracted from the images are considered.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Computing, University of Buckingham, Buckingham, MK18 1EG, U.K.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

A flow chart illustrating the randomised balanced cross validation process of selecting the training and test groups. This process was repeated 15 times to calculate the average diagnostic performance of the SVM in each one of the 8 main images’ groups.
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Figure 5: A flow chart illustrating the randomised balanced cross validation process of selecting the training and test groups. This process was repeated 15 times to calculate the average diagnostic performance of the SVM in each one of the 8 main images’ groups.

Mentions: In order to address the class imbalance problem between benign (112 cases) and malignant (75 cases) in each data set and to develop a fair classification model for both cases, we randomly sampled 50 images of benign and 50 images of malignant tumours, totalling 100 images for training and testing the SVM. The sampling (without replacement) was performed using the Randsample function in Matlab (50 benign with Randsample (112, 50), and 50 malignant Randsample (75, 50)). For evaluation of performance, we employed a stratified 50-fold cross validation, which means applying the leave-one-out strategy to utilise the use of training examples. In an iterative process, one partition, i.e. two samples (one benign and one malignant), was taken as the test examples and the rest for training the SVM (Fig. 5). We repeated this process 15 times with a different random selection of 100 images (50 benign and 50 malignant) to reduce the random effect.


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.

Khazendar S, Sayasneh A, Al-Assam H, Du H, Kaijser J, Ferrara L, Timmerman D, Jassim S, Bourne T - Facts Views Vis Obgyn (2015)

A flow chart illustrating the randomised balanced cross validation process of selecting the training and test groups. This process was repeated 15 times to calculate the average diagnostic performance of the SVM in each one of the 8 main images’ groups.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402446&req=5

Figure 5: A flow chart illustrating the randomised balanced cross validation process of selecting the training and test groups. This process was repeated 15 times to calculate the average diagnostic performance of the SVM in each one of the 8 main images’ groups.
Mentions: In order to address the class imbalance problem between benign (112 cases) and malignant (75 cases) in each data set and to develop a fair classification model for both cases, we randomly sampled 50 images of benign and 50 images of malignant tumours, totalling 100 images for training and testing the SVM. The sampling (without replacement) was performed using the Randsample function in Matlab (50 benign with Randsample (112, 50), and 50 malignant Randsample (75, 50)). For evaluation of performance, we employed a stratified 50-fold cross validation, which means applying the leave-one-out strategy to utilise the use of training examples. In an iterative process, one partition, i.e. two samples (one benign and one malignant), was taken as the test examples and the rest for training the SVM (Fig. 5). We repeated this process 15 times with a different random selection of 100 images (50 benign and 50 malignant) to reduce the random effect.

Bottom Line: Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image.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).The accuracy improves if texture related LBP features extracted from the images are considered.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Computing, University of Buckingham, Buckingham, MK18 1EG, U.K.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus