Ray Casting is a direct volume rendering technique for visualizing 3D arrays of sampled data. It has vital applications in medical and biological imaging. Nevertheless, it is inherently open to cluttered classification results. It suffers from overlapping transfer function values and lacks a sufficiently powerful voxel parsing mechanism for object distinction. In this work, we are proposing an image processing based approach towards enhancing ray casting technique for object distinction process. The rendering mode is modified to accommodate masking information generated by a K-means based hybrid segmentation system. An effective set of image processing techniques are creatively employed in construction of a generic segmentation system capable of generating object membership information. Preprocessing, initialization of cluster centers, clustering, statistical optimization, edge detection a analysis and spatial adjustment are respectively the six main segmentation phases.
['Arash Azim Zadeh Irani', 'Bahari Belaton']
An Automated Adaption of K-means Based Hybrid Segmentation System into Direct Volume Rendering Object Distinction Mode for Enhanced Visualization Effect