Opacity Transfer Function Optimization for Volume-rendered Computed Tomography Images of the Prostate1

Jani, Ashesh B.; Irick, John-Stockton; Pelizzari, Charles A.

doi:10.1016/j.acra.2005.03.054

Cited by 6 publications

(7 citation statements)

References 52 publications

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“…α was iteratively modified until the partial surface corresponding to this ROI was optimally matched to the 'true' target (defined in this investigation as the target V defined by an expert observer using the axial images). In all cases in this manuscript, α refers to the opacity function which has been optimized using this process to minimize e. Though this optimization method has been described previously [37,39], a brief mention is provided herein:…”

Section: Methodsmentioning

confidence: 99%

“…Optimization typically produced values of e which were 1-3 VU lower than that obtained using the starting/manually chosen α. Of note, this optimization using partial surface matching is to be distinguished from other commonly used methods, such as with construction of first and second-derivative information and the histogram volume structure [39,42].…”

Section: Methodsmentioning

confidence: 99%

“…Perspective volume rendered images were produced on each of these datasets using established techniques [22,[35][36][37][38][39]. Essentially, the user defines a focal point, near-clipping plane, far-clipping plane, and opacity function; virtual rays are cast into the dataset and accumulate opacity with respect to this opacity function.…”

Section: Methodsmentioning

confidence: 99%

“…Prior investigations suggest the utility of CT volume rendered images for regions with sharp contrast between the structure of interest and surrounding tissue [36][37][38]. Prior reports also document the limited role of CT volume rendered images in the prostate, due to the blurriness of the edge of the prostate on CT [39].…”

Section: Introductionmentioning

confidence: 95%

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Optimization of opacity function for computed tomography volume rendered images of the prostate using magnetic resonance reference volumes

et al. 2007

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Objective To enhance the opacity function for computed tomography (CT)-based prostate volume rendered (VR) images using reference magnetic resonance (MR) images. Materials and Methods After simulating on phantoms, ten patients with CT and MR datasets were identified/ registered. Optimal VR images were produced, and intra-modality and cross-modality errors [mean distance in voxel units (VU) from VR partial surface to target] were computed. On the CT VR images, a global transformation was computed to relate the opacity functions obtained using CT reference volumes with those obtained using MR reference volumes. Results The prostate was better visualized on MR than CT, as the intra-modality errors obtained using MR (2.78 ± 1.02 VU) were lower than on CT (3.68 ± 2.15 VU). Despite similar qualitative results as the intramodality CT images, the cross-modality errors obtained using the individual patient data (2.77 ± 0.99 VU) and using the global transformation (2.89 ± 0.99 VU) were each closer (P = 0.010 and P = 0.011, respectively) to the intra-modality MR errors than were the intra-modality CT errors. Conclusion By optimizing the opacity function using MR reference images, CT VR images of the prostate could closely represent the prostate defined on MR; furthermore, this process can be approximated by a simple transformation. Overall, the errors were reduced to a level (<3 VU) justifying further exploration in CT-based radiotherapy treatment planning.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

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Optimization of opacity function for computed tomography volume rendered images of the prostate using magnetic resonance reference volumes

et al. 2007

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“…The condition in rendering indicates a unit area, volume which may be a 3D cubic factor array. To develop a 3D data set volume rendering technique is one of the optimized method which is developed here [4]. For imaging development in medical field, rendering is carried out by direct class rendering and its approach is said to be successful and hard to find current approach is insufficient.…”

Section: Introductionmentioning

confidence: 99%

Optimal View Point Selection in Direct Volume Rendering for 3D Visualization

Gunasekaran¹,

MeenakshiSundram²

2017

IJIES

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3D visualization is the recent research need to be able to view the model from various angles so as to see data that is obscured by other data. While visualizing the data, view point selection is one of the major challenge which define the viewpoint information to select best viewpoints. This paper introduces teaching learning opacity based optimization (TLOO) and Kalman filter for selecting optimal viewpoint. The structural features of 3D volume dataset such as opacity and luminance is extracted for selecting optimal viewpoint position from that structural features. TLOO optimization algorithm identifies the voxels and standard information using view point evolution function. Then, kalman filter steers the optimization process with the help of cost function. The proposed flow of the technique is simple and very effective for selecting optimal viewpoints in 3D images. Computational complexity and the quality metrics are analyzed that provided maximum output efficiency.

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