Texture Analysis in the Characterisation of Ovarian Lesions: Use of Synthetic Minority Oversampling

T2 weighted images of ovarian lesions have been assessed using texture analysis. Uneven group sizes, which can cause bias in statistical models leading to over-fitting of the majority class, were avoided by employing synthetic minority oversampling methods. Significant differences between groups were noted for 12/16 texture parameters and a multinomial logistic regression model utilised 4 parameters (f2, f14, f15, f16) in achieving a diagnostic accuracy of 71% for a training dataset and 61% for a test dataset.

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