Ray grouping based ray casting for visualization of medical data

Mehaboobathunnisa, R.; Thasneem, A.A. Haseena; Sathik, M. Mohamed

doi:10.1109/icctide.2016.7725332

Cited by 1 publication

(1 citation statement)

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“…It emits light rays from each pixel of the 2D imaging space, traverses the entire image sequence and sample the color information to composite the final rendering image. The ray‐casting algorithm has been widely applied in medical datasets such as CT and MRI 15,36,37 and seismic cube data 16 . However, most of the volume visualization system needs to run on high‐end graphics cards due to the high computational cost of volume rendering, and may still have low frame per second with complex algorithm such as ray tracing 3 .…”

Section: Related Workmentioning

confidence: 99%

Multiuser collaborative illustration and visualization for volumetric scientific data

et al. 2020

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Multiuser can collaboratively complete complex visualization tasks that cannot be completed by a single user. Although multiuser collaboration system has made great progress, there are many challenges in the collaborative visualization of 3D volumetric scientific data due to the difficulties in multiuser collaboration, and collaborative slice analysis. This article proposes a client‐server based collaborative visualization system, which consists of a 3D volume explorer and a 2D slice analyzer, to help domain experts to fully utilize their background domain knowledge to illustrate different parts of the data. For example, the brain surgeon expert, pulmonologist, and cardiologist can visualize and analyze the different subsets of the volumetric scientific data, that is, the corresponding subvolumes of the brain, heart, lungs, and blood vessels. It also allows taking full advantage of the hardware resources, because all the computation intensive tasks especially for the whole data rendering can be allocated to the powerful server while the light‐weight tasks can be allocated to the portable clients. Besides, we design a seed point tracing algorithm based on flood fill algorithm to illustrate the slice more efficiently. We evaluate the system by collecting the feedback from domain experts and the people who are unfamiliar with data computation or visualization. The evaluation shows that the 3D volume explorer and the 2D slice analyzer are capable of supporting peer‐expert discussion and medical case teaching, respectively.

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Section: Related Workmentioning

confidence: 99%