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

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.

Keywords: Decision support techniques; Support Vector Machines; ovarian cancer; ovarian neoplasm; ultrasonography.

Fig. 1. Pre-processing the image before segmentation.

The Absolute Difference is a basic image processing operation that takes the absolute value of the difference between the values of the two corresponding pixels I1(i) and I2(i), from the two input images I1 (here is the filtered image) and I2 (here is the negative of the filtered image) r(i) = ∣I1(i) - I2(i)∣ where r (i) represents the ith pixel in the result image. We apply the absolute difference operation on the de-noised image from the NL-means filtering step and its negative image. This means that r(i) = ∣Intensity_max - 2×I(i)∣

Fig. 2. The NL-means de-noising method.

Fig. 3. An example of the features transformation using pre-processing methods (left images column) and corresponding LBP processing (right images column). As a result 7 extra images were created from each original image.

Fig. 4. Description of an ultrasound image of a functional cyst using a concatenated Local Binary Pattern histogram.

Fig. 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.