The two major volume visualization methods used in biomedical applications are Maximum Intensity Projection (MIP)
and Volume Rendering (VR), both of which involve the process of creating sets of 2D projections from 3D images.
We have developed a new method for very fast, high-quality volume visualization of 3D biomedical images, based on
the fact that the inverse of this process (transforming 2D projections into a 3D image) is essentially equivalent to
tomographic image reconstruction.
This new method uses the 2D projections acquired by the scanner, thereby obviating the need for the two
computationally expensive steps currently required in the complete process of biomedical visualization, that is, (i)
reconstructing the 3D image from 2D projection data, and (ii) computing the set of 2D projections from the
reconstructed 3D image
As well as improvements in computation speed, this method also results in improvements in visualization quality, and in
the case of x-ray CT we can exploit this quality improvement to reduce radiation dosage.
In this paper, demonstrate the benefits of developing biomedical visualization techniques by directly processing the
sensor data acquired by body scanners, rather than by processing the image data reconstructed from the sensor data. We
show results of using this approach for volume visualization for tomographic modalities, like x-ray CT, and as well as
Segmentation of three-dimensional anatomical brain images into tissue classes has applications in both clinical and research settings. This paper presents the implementation and quantitative evaluation of a four-phase three-dimensional active contour implemented with a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to speed up numerical computation and avoid the need for a priori information. This random initialization ensures robustness of the method to variation of user expertise, biased a priori information and errors in input information that could be influenced by variations in image quality. Experimentation on three MRI brain data sets showed that an optimal partitioning successfully labeled regions that accurately identified white matter, gray matter and cerebrospinal fluid in the ventricles. Quantitative evaluation of the segmentation was performed with comparison to manually labeled data and computed false positive and false negative assignments of voxels for the three organs. We report high accuracy for the two comparison cases. These results demonstrate the efficiency and flexibility of this segmentation framework to perform the challenging task of automatically extracting brain tissue volume contours.