Confocal volume rendering: Fast segmentation-free visualization of internal structures

被引:5
|
作者
Mullick, R [1 ]
Bryan, N [1 ]
Butman, J [1 ]
机构
[1] NIH, Dept Diagnost Radiol, Ctr Clin, Bethesda, MD 20892 USA
关键词
volume rendering; segmentation; visualization; diagnostic evaluation; screening; surgery pre-planning;
D O I
10.1117/12.383084
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Volume rendering is now a common tool for multi-dimensional data exploration in biology, medicine, meteorology, geology, material science, and other fields. In order to perform volume rendering, users are often forced to preprocess and segment their data. This step of processing before visualization often inhibits the use of volume rendering as it can be quite cumbersome and can also introduce undesirable artifacts. In order to enhance the use of direct volume visualization, powerful, yet easy-to-use methods need to be developed. In this paper, we present an approach that offers the user data-dependent control over the focal region (in physical depth terms) of the visualization. This approach enables the user to easily visualize interior structures in the dataset by controlling physically defined parameters, without performing segmentation.
引用
收藏
页码:70 / 76
页数:7
相关论文
共 27 条
  • [1] Visualization of volume data in confocal microscopy: Comparison and improvements of volume rendering methods
    Lucas, L.
    Gilbert, N.
    Ploton, D.
    Bonnet, N.
    Journal of Microscopy, 1996, 181 (03): : 238 - 252
  • [2] Visualization of volume data in confocal microscopy: Comparison and improvements of volume rendering methods
    Lucas, L
    Gilbert, N
    Ploton, D
    Bonnet, N
    JOURNAL OF MICROSCOPY-OXFORD, 1996, 181 : 238 - 252
  • [3] Improved volume rendering for the visualization of living cells examined with confocal microscopy
    Enloe, LC
    Griffing, LR
    VISUAL DATA EXPLORATION AND ANALYSIS VII, 2000, 3960 : 385 - 392
  • [4] VISUALIZATION OF MR ANGIOGRAPHIC DATA WITH SEGMENTATION AND VOLUME-RENDERING TECHNIQUES
    HU, XP
    ALPERIN, N
    LEVIN, DN
    TAN, KK
    MENGEOT, M
    JMRI-JOURNAL OF MAGNETIC RESONANCE IMAGING, 1991, 1 (05): : 539 - 546
  • [5] Cascaded Regression Neural Nets for Kidney Localization and Segmentation-free Volume Estimation
    Hussain, Mohammad Arafat
    Hamarneh, Ghassan
    Garbi, Rafeef
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2021, 40 (06) : 1555 - 1567
  • [6] Fast visualization of object contours by non-photorealistic volume rendering
    Csábfalvi, Balázs
    Mroz, Lukas
    Hauser, Helwig
    König, Andreas
    Gröller, Eduard
    2001, Blackwell Publishing Ltd. (20)
  • [7] Fast visualization of object contours by non-photorealistic volume rendering
    Csébfalvi, B
    Mroz, L
    Hauser, H
    König, A
    Gröller, E
    COMPUTER GRAPHICS FORUM, 2001, 20 (03) : C452 - +
  • [8] Segmentation-free direct tumor volume and metabolic activity estimation from PET scans
    Taghanaki, Saeid Asgari
    Duggan, Noirin
    Ma, Hillgan
    Hou, Xinchi
    Celler, Anna
    Benard, Francois
    Hamarneh, Ghassan
    COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2018, 63 : 52 - 66
  • [9] A fast segmentation-free fully automated approach to white matter injury detection in preterm infants
    Mukherjee, Subhayan
    Cheng, Irene
    Miller, Steven
    Guo, Ting
    Chau, Vann
    Basu, Anup
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2019, 57 (01) : 71 - 87
  • [10] A fast segmentation-free fully automated approach to white matter injury detection in preterm infants
    Subhayan Mukherjee
    Irene Cheng
    Steven Miller
    Ting Guo
    Vann Chau
    Anup Basu
    Medical & Biological Engineering & Computing, 2019, 57 : 71 - 87