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.. Statistical analysis We calculated the average sensitivity, specificity and accuracy of the SVM for each of the 15 cycles. The same PD173074 test was performed for each of the 8 main image groups. Medcalc software (MedCalc Software bvba, version 12, 2013, Belgium) was used to calculate the difference in accuracies between different groups. The two-tailed t test was used to compare means and p MRIP images (0.62, 95%CI: 0.59-0.65) (difference of 0.04, 95%CI: -0.001 �C 0.08, p = 0.06). When the LBP was applied, the latter difference in SVM average accuracy between ROI and whole image characterization increased to 0.07 (95%CI: 0.04-0.08, p selleck chemical to 0.77, p = 0.03). In our data, we have tried the kNN (k-Nearest Neighbours) classifier, instead of SVM, using Euclidean distance metric when k = 1. However we found that the performance of the SVM was significantly better (Table III). We believe that this might be due to the better performance of an SVM with higher dimensionality. Table III. Diagnostic performance of the kNN on images processed using a Local Binary Pattern operator. Euclidean distance metric when k = 1. Discussion In this study we have shown that computer-based image processing technologies can automatically categorise static B mode ultrasound images as being derived from either benign or malignant ovarian cysts.