Image analysis of neurological NMR data is often an easier undertaking when non-cerebral tissue compartment voxels are removed from the NMR image dataset. This preprocessing step is often called 'skull stripping'. The simple but robust technique formulated and presented in this paper utilizes a combination of mathematical morphology and statistical segmentation techniques.Non-tissue background voxels are deemed to possess a Rayleigh distribution and consequently removed using an adaptive region dividing technique. Further processing automatically identifies a set of voxels that act as a test slice to determine whether the cerebral tissue compartment voxels have been fully separated during subsequent morphological processing. This set is used as a test to terminate an iterative morphological processing scheme to disconnect cerebral from non-cerebral voxels.The method has been successfully applied to 9 NMR datasets of varying quality with low inter-slice resolution. It therefore appears that this approach should be sufficiently robust to be useful for the statistical analysis of routine clinical NMR data.
In this paper we present a textural feature analysis applied to a medical image segmentation problem where other methods fail, i.e. the localization of thrombotic tissue in the aorta. This problem is extremely relevant because many clinical applications are being developed for the computer assisted, image driven planning of vascular intervention, but standard segmentation techniques based on edges or gray level thresholding are not able to differentiate thrombus from surrounding tissues like vena, pancreas having similar HU average and noisy patterns [3,4]. Our work consisted in a deep analysis of the texture segmentation approaches used for CT scans, and on experimental tests performed to find out textural features that better discriminate between thrombus and other tissues. Found that some Run Length codes perform well both in literature and experiments, we tried to understand the reason of their success suggesting a revision of this approach with feature selection and the use of specifically thresholded Run Lengths that improves the discriminative power of measures reducing the computational cost.
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