Irani, Arash Azim Zadeh
(2013)
Segmentation Assisted Object Distinction For Direct
Volume Rendering.
PhD thesis, Universiti Sains Malaysia.
Abstract
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 research work, we are proposing an image
processing based approach towards enhancing ray casting technique’s object
distinction process. The ray casting architecture is modified to accommodate object
membership information generated by a K-means based hybrid segmentation
algorithm. Object membership information is assigned to cubical vertices in the form
of ID tags. An intra-object buffer is devised and coordinated with inter-object buffer,
allowing the otherwise global rendering module to embed multiple local (secondary)
rendering processes. A local rendering process adds two advantageous aspects to
global rendering module. First, depth oriented manipulation of interpolation and
composition operations that lead to freedom of interpolation method choice based on
the number of available objects in various volumetric depths, improvement of LOD
(level of details) for desired objects and reduced number of required mathematical
computations. Second, localization of transfer function design that enables the
utilization of binary (non-overlapping) transfer functions for color and opacity
assignment. A set of image processing techniques are creatively employed in the
design of K-means based hybrid segmentation algorithm.
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