A model-based adaptive method for speckle noise reduction in ultrasound images of ovarian tumours: a new approach

Ultrasound imaging is widely used in medical diagnostics. The existence of speckle noise tends to impair ultrasound image quality, which has a negative effect on the computer-aided diagnostic pipeline. As a result, a content-preserving noise reduction is an essential part of ultrasound image pre-processing. This paper argues that conventional one-fit-all preprocessing methods on all images irrespective of their quality and/or their content have many limitations. The paper demonstrates that the negative effects of the speckle noise are more significant in regions where solid tissues are present. Consequently, we propose an adaptive approach of using trained classification models to detect such regions within the image and targeting the speckle noise of the detected regions instead of the whole image. The detection is achieved by placing a sliding window over the image and feeding individual windows to a trained classifier. In this study, we first analyse the content of the images to identify the complexity of the speckle noise by training a linear support vector machine classifier on histogram-based measurements such as skewness and kurtosis to determine whether the image partially or fully needs pre-processing. To evaluate the effectiveness of the new adaptive pre-processing methods, a hybrid two-model solution in which the first trainable model decides if an image requires pre-processing or not and applies it respectively on the whole image. The second model takes a step further to check which parts of the images requires pre-processing and adaptively applies it using the block-based trainable system. The results, based on 138 benign and 104 malignant ovarian ultrasound images, show that the two models performed better than other state-of-the-art pre-processing techniques, which confirms the need for the adaptive system that applies pre-processing only when needed.