高级检索
当前位置: 首页 > 详情页

Perception enhancement using importance-driven hybrid rendering for augmented reality based endoscopic surgical navigation

文献详情

资源类型:
WOS体系:
Pubmed体系:

收录情况: ◇ SCIE ◇ EI

机构: [1]Beijing Inst Technol, Sch Opt & Elect, Beijing Engn Res Ctr Mixed Real & Adv Display, Beijing 100081, Peoples R China [2]Beijing Inst Technol, Sch Software, Beijing 100081, Peoples R China [3]Beijing Tongren Hosp, Dept Otolaryngol Head & Neck Surg, Beijing 100730, Peoples R China
出处:
ISSN:

摘要:
Misleading depth perception may greatly affect the correct identification of complex structures in image-guided surgery. In this study, we propose a novel importance-driven hybrid rendering method to enhance perception for navigated endoscopic surgery. First, the volume structures are enhanced using gradient-based shading to reduce the color information in low-priority regions and improve the distinctions between complicated structures. Second, an importance sorting method based on the order-independent transparency rendering is introduced to intensify the perception of multiple surfaces. Third, volume data are adaptively truncated and emphasized with respect to the perspective orientation and the illustration of critical information for viewing range extension. Various experimental results prove that with the combination of volume and surface rendering, our method can effectively improve the depth distinction of multiple objects both in simulated and clinical scenes. Our importance-driven surface rendering method demonstrates improved average performance and statistical significance as rated by 15 participants (five clinicians and ten non-clinicians) on a five-point Likert scale. Further, the average frame rate of hybrid rendering with thin-layer sectioning reaches 42 fps. Given that the process of the hybrid rendering is fully automatic, it can be utilized in real-time surgical navigation to improve the rendering efficiency and information validity. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

基金:
语种:
被引次数:
WOS:
PubmedID:
中科院(CAS)分区:
出版当年[2017]版:
大类 | 2 区 医学
小类 | 2 区 光学 2 区 核医学 3 区 生化研究方法
最新[2025]版:
大类 | 3 区 医学
小类 | 2 区 生化研究方法 3 区 光学 3 区 核医学
JCR分区:
出版当年[2016]版:
Q1 OPTICS Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Q2 BIOCHEMICAL RESEARCH METHODS
最新[2023]版:
Q2 BIOCHEMICAL RESEARCH METHODS Q2 OPTICS Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子: 最新[2023版] 最新五年平均 出版当年[2016版] 出版当年五年平均 出版前一年[2015版] 出版后一年[2017版]

第一作者:
第一作者机构: [1]Beijing Inst Technol, Sch Opt & Elect, Beijing Engn Res Ctr Mixed Real & Adv Display, Beijing 100081, Peoples R China
通讯作者:
推荐引用方式(GB/T 7714):
APA:
MLA:

资源点击量:25471 今日访问量:0 总访问量:1498 更新日期:2025-06-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 首都医科大学附属北京同仁医院 技术支持:重庆聚合科技有限公司 地址:北京市东城区东交民巷1号(100730)