Image based atlases for rats brain have a significant impact on pre-clinical research. In this project we acquired T1-weighted images from Wistar rodent brains with fine 59μm isotropical resolution for generation of the atlas template image. By applying post-process procedures using a semi-automatic brain extraction method, we delineated the brain tissues from source data. Furthermore, we applied a symmetric group-wise normalization method to generate an optimized template of T1 image of rodent brain, then aligned our template to the Waxholm Space. In addition, we defined several simple and explicit landmarks to corresponding our template with the well known Paxinos stereotaxic reference system. Anchoring at the origin of the Waxholm Space, we applied piece-wise linear transformation method to map the voxels of the template into the coordinates system in Paxinos' stereotoxic coordinates to facilitate the labelling task. We also cross-referenced our data with both published rodent brain atlas and image atlases available online, methodologically labelling the template to produce a Wistar brain atlas identifying more than 130 structures. Particular attention was paid to the cortex and cerebellum, as these areas encompass the most researched aspects of brain functions. Moreover, we adopted the structure hierarchy and naming nomenclature common to various atlases, so that the names and hierarchy structure presented in the atlas are readily recognised for easy use. It is believed the atlas will present a useful tool in rodent brain functional and pharmaceutical studies.
Our previous work to segment the complete ventricular system from T1-weighted and SPGR MR images is extended here to deal with T2-weighted MR images. For each ventricle, a region of interest is determined first and its local statistics is calculated to find the intensity ranges of cerebrospinal fluid, grey matter and white matter. Then, region growing is performed in each ventricle based on the calculated statistics. During region growing, anti-leakage conditions are checked to prevent growing into extraventricular spaces. With the incorporation of domain knowledge, radiological properties and geometrical constraints, the algorithm provides a means for the extraction of the ventricular system from T2-weighted MR images. Initial experimental results are presented with the extracted third and fourth ventricles.
KEYWORDS: Medical imaging, Java, Visualization, Image processing, Medical research, 3D image processing, Visual process modeling, 3D modeling, Image segmentation, Analytical research
Medical imaging research and clinical applications usually require combination and integration of different
technology from image processing to realistic visualization to user-friendly interaction. Researchers with
different background and from various research areas have been using numerous types of hardware,
software and environments to produce their research results. It is unusual that students must build their
working and testing tools from scratch again and again. A generic and flexible medical imaging and
visualization toolkit would be helpful in medical research and educational institutes to reduce redundant
development work and hence prompt their research efficiency. In our lab, we have developed a Medical
Imaging and Visualization Toolkit (BIL-kit), which is a set of comprehensive libraries as well as a number
of interactive tools. It covers a wide range of fundamental functions from image conversion and
transformation, image segmentation and analysis, to geometric model generation and manipulation, all the
way up to 3D visualization and interactive simulation. The toolkit design and implementation emphasize
the reusability and flexibility. BIL-kit is implemented by using Java language because of its advantage in
platform independent, so that the toolkit will work in hybrid and dynamics research and educational
environments. This also allows the toolkit to extend its usage in web based application development. BILkit
is a suitable platform for researchers and students to develop visualization and simulation prototypes as
well as it can also be used for development of clinical applications.
This paper introduces NeuroBase, an atlas-assisted neuroimaging system. NeuroBase is a flexible, affordable and cross-platform system capable to process multiple datasets. The design, functionality and numerous tools of NeuroBase are presented. Two novel paradigms are introduced here: warping symmetry with respect to any reference dataset or atlas, and triplanar mosaic presentation. We also report our preliminary experience in the use of NeuroBase for various applications including neuro education, neuro radiology, brain mapping, stererotactic functional neurosurgery and multi-modal visualization.
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