As whole body MRI (WB-MRI) gains currency, the data this class of
technique generates presents new challenges for the imaging
community. One acquisition protocol currently being applied with
considerable success entails imaging the subject in a number of
successive coronal sections, resulting in a high resolution, gap
free, full body acquisition. However this technique often results
in considerable greylevel offsets between adjacent coronal
sections. To make the images suitable for the application of
automated image analysis procedures these discontinuities in the
grey data must be alleviated. We examine the issues related to
this problem, and present a solution based on histogram rescaling,
which is designed to correct for the non-uniformities while
preserving the integrity of the data histogram so that it can be
used robustly in later processing steps. The final datasets
reconstructed from the resampled coronal sections exhibit superior
greyscale homogeneity, visually and in statistical measures, and
the image segmentation results achieved using this corrected data
are consistently more robust and more accurate than those arrived
at using the original raw data. The approach has been tested and
successfully validated on a database of sixty two WB-MRI datasets.
Magnetic Resonance Cholangiopancreatography (MRCP) is a type of MR imaging which utilizes protocols designed to enhance stationary fluids in the imaged volume. In this way it visualizes the pancreatobiliary tract by highlighting the bile and pancreatic juices in the system. Current practice sees this data being assessed directly, with little or no processing being performed prior to review. MRCP data presents three main difficulties when it comes to image processing. The first is the relatively noisy nature of the data. Second is its low spatial resolution, especially in the inter-slice direction. And third, the variability observed between MRCP studies, which makes consistent results difficult to attain. This paper describes the initial phase of research which aims to develop assistive image analysis techniques to aid in the interpretation of MRCP data. The first stage in this process is the robust segmentation of the pancreatobiliary system. To this end a segmentation procedure has been developed using an approach based on the tools and techniques of the mathematical morphology. This paper examines the task at hand and presents initial results, describing and assessing the segmentation approach developed.
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