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Author(s) :
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Abstract :
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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 Ultrasonic images could have prognostic
implications for detecting affected tissue, and thus in creating
semi-automatic tools to assist the experts. We also try not to
compensate for the machine settings as was done in former
experiments because this compensation is often machine dependent
and quite tricky since we have to guess up to some degree what
goes on inside of the Ultrasound machine. We 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.
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