Dynamic SPECT is a novel technique in nuclear medicine imaging. To find coherent structures within the dataset is the most important part of analyzing dSPECT data. Usually the observer focuses on a certain structure or an organ, which is to be identified and outlined. We use a user-guided method where a starting point is interactively selected whcih is also used to identify the object or structure. To find the starting point for segentation we search for the voxel having the maximum intensity in the dataset along the eye beam. In the situation where the data is segmented by region growing, we render both, the segmentation result and the original data in one view. The segmentation result is displayed as a wire mesh and fades over the voluem rendered original data. We use this hybrid rendering method in order to enable the user to validate the correctness of the sementation process. So it is possible to compare the two objects in one rendition.
In this paper we present a method for interactive analysis of non-segmented medical volume data. We discuss both different rendering methods for visualization and different possibilities for interaction in relation to segmentation results. Furthermore, the adaptive region growing approach is applied to both segmentation of a structure of interest, as well as generation of transfer function for volume rendering of the same structure. The adaptive region growing method is based on the statistical evaluation of 3D-neighbourhood. The method is used for determination of a homogeneity criterion for the structure of interest. Subsequently this criterion is used for segmenting of data and for generating of an initial transfer function for volume rendering. We utilize this for displaying a hybrid 3D-visualization of the segmented structure and the specific gray-value interval of original data. Based on this rendering we discuss possibilities for user-guided validation of segmentation results, based on the variation of several rendering parameters.
Successful extraction of small vessels in DSA images requires inclusion of prior knowledge about vessel characteristics. We developed an active double contour (ADC) that uses a vessel template as a model. The template is fitted to the vessel using an adapted ziplock snake approach based on two user-specified end locations. The external energy terms of the ADC describe an ideal vessel with projections changing slowly their course, width and intensity. A backtracking ability was added that enables overturning local decisions that may cause the ziplock snake to be trapped in a local minimum. This is because the optimization of the ADC is carried out locally. If the total energy indicates such case, vessel boundary points are removed and the ziplock process starts again without this location in its actual configuration. The method was tested on artificial data and DSA data. The former showed good agreement between artificial vessel and segmented structure at an SNR as low as 1.5:1. Results from DSA data showed robustness of the method in the presence of noise and its ability to cope with branchings and crossings. The backtracking was found to overcome local minima of the energy function at artefacts, vessel crossings and in regions of low SNR.
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