An evaluation of brain tissue classification in non-compensated ultrasound images

In this article we present new results on the classification of the neonatal “White Matter Damage” brain disease. One of the common diagnostic methods nowadays used in clinical practice is the visual inspection of Ultrasound images of the neonatal brain. Given the poor image quality of Ultrasound images and the different machine settings used in practice, this diagnosis highly depends on the interpretation of the medical doctor and is subjective to some degree. In this paper we investigate if the texture present in the images could have prognostic implications for detecting affected tissue, and thus help us in creating semi-automatic tools to assist the experts. We try not to compensate for the machine settings as was done in former experiments because this compensation is often machine dependent and quite tricky. We have to guess up to some degree what goes on inside of the Ultrasound machine. As a main contribution will show it is possible to get very high classification rates without this preprocessing which is a great step forward in the quantitative analysis of the images.