An Augmented Reality System Using Improved-Iterative Closest Point Algorithm for On-Patient Medical Image Visualization

Wu, Ming-Long; Chien, Jong-Chih; Wu, Chieh‐Tsai; Lee, Jiann-Der

doi:10.3390/s18082505

Cited by 33 publications

(30 citation statements)

References 24 publications

(30 reference statements)

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“…One area of possible improvement for this AR system is in the degree of accurate registration between the virtual and physical models. Our average error of 2.47 millimeter, however, is similar to that demonstrated by other authors using comparable AR systems . In another study using the HoloLens platform by Wu et al, the average error was within 3 millimeters.…”

Section: Discussionsupporting

confidence: 90%

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Development of augmented‐reality applications in otolaryngology–head and neck surgery

et al. 2019

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Objectives/Hypothesis: Augmented reality (AR) allows for the addition of transparent virtual images and video to one's view of a physical environment. Our objective was to develop a head-worn, AR system for accurate, intraoperative localization of pathology and normal anatomic landmarks during open head and neck surgery.Study Design: Face validity and case study. Methods: A protocol was developed for the creation of three-dimensional (3D) virtual models based on computed tomography scans. Using the HoloLens AR platform, a novel system of registration and tracking was developed. Accuracy was determined in relation to actual physical landmarks. A face validity study was then performed in which otolaryngologists were asked to evaluate the technology and perform a simulated surgical task using AR image guidance. A case study highlighting the potential usefulness of the technology is also presented.Results: An AR system was developed for intraoperative 3D visualization and localization. The average error in measurement of accuracy was 2.47 AE 0.46 millimeters (1.99, 3.30). The face validity study supports the potential of this system to improve safety and efficiency in open head and neck surgical procedures.Conclusions: An AR system for accurate localization of pathology and normal anatomic landmarks of the head and neck is feasible with current technology. A face validity study reveals the potential value of the system in intraoperative image guidance. This application of AR, among others in the field of otolaryngology-head and neck surgery, promises to improve surgical efficiency and patient safety in the operating room.

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Section: Discussionsupporting

confidence: 90%

“…Our average error of 2.47 millimeter, however, is similar to that demonstrated by other authors using comparable AR systems . In another study using the HoloLens platform by Wu et al, the average error was within 3 millimeters. Mahmoud et al reported virtual and physical model alignment of approximately 5 millimeters.…”

Section: Discussionsupporting

confidence: 90%

Development of augmented‐reality applications in otolaryngology–head and neck surgery

et al. 2019

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“…Achieving high-quality alignment is the critical step in model differencing, and CloudCompare's registration methods have been tested and used in the literature [26,[139][140][141]. The process is iterative, with the alignment becoming better with each iteration.…”

Section: Quantitative Differencing Using Cloudcomparementioning

confidence: 99%

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Nagle-McNaughton

Cox

2019

Remote Sensing

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Repeat photogrammetry is increasingly the go-too tool for long-term geomorphic monitoring, but quantifying the differences between structure-from-motion (SfM) models is a developing field. Volumetric differencing software (such as the open-source package CloudCompare) provides an efficient mechanism for quantifying change in landscapes. In this case study, we apply this methodology to coastal boulder deposits on Inishmore, Ireland. Storm waves are known to move these rocks, but boulder transportation and evolution of the deposits are not well documented. We used two disparate SfM data sets for this analysis. The first model was built from imagery captured in 2015 using a GoPro Hero 3+ camera (fisheye lens) and the second used 2017 imagery from a DJI FC300X camera (standard digital single-lens reflex (DSLR) camera); and we used CloudCompare to measure the differences between them. This study produced two noteworthy findings: First, volumetric differencing reveals that short-term changes in boulder deposits can be larger than expected, and that frequent monitoring can reveal not only the scale but the complexities of boulder transport in this setting. This is a valuable addition to our growing understanding of coastal boulder deposits. Second, SfM models generated by different imaging hardware can be successfully compared at sub-decimeter resolution, even when one of the camera systems has substantial lens distortion. This means that older image sets, which might not otherwise be considered of appropriate quality for co-analysis with more recent data, should not be ignored as data sources in long-term monitoring studies.Despite these improvements in protocols for data collection and analysis, quantifying change via repeat photogrammetry remains a challenge, generally requiring manipulation of digital terrain models (DTMs) using Geographic Information Systems (GIS) [45,46]. But recent innovations in 3D differencing software, as implemented in the open-source package CloudCompare (www.danielgm.net/cc/) enable rapid, objective, and replicable quantitative differencing [47]. CloudCompare is a cross-platform 3D point cloud and mesh processing package, which provides a suite of features for direct comparison of dense point clouds (>10 million points) and meshes on standard consumer computer hardware [48,49]. Accessibility and ease of use make CloudCompare an attractive tool, and it is applied increasingly in a variety of fields, including mapping [50,51], archaeology [52,53], and geomorphology [54][55][56][57].Another issue relevant to long term monitoring studies is how to deal with repeat imagery captured using different kinds of hardware, with different resolutions and/or distortion levels. The ultra-wide-angle (fisheye) lenses mounted on the popular consumer camera brand GoPro capture a~120 • field of view, but have barrel distortion due to their short focal length. Despite their limitations, GoPro cameras are popular tools for acquiring UAV imagery [24,31,[58][59][60], they are relatively inexpensive, are light enou...

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“…Such systems require a large amount of high-resolution textures to provide an immersive sensation for users. Such high-resolution textures may even be required to be generated in real-time [4,5].…”

Section: Introductionmentioning

confidence: 99%

Survey of Procedural Methods for Two-Dimensional Texture Generation

Dong

Liu

Yao³

et al. 2020

Sensors

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Textures are the most important element for simulating real-world scenes and providing realistic and immersive sensations in many applications. Procedural textures can simulate a broad variety of surface textures, which is helpful for the design and development of new sensors. Procedural texture generation is the process of creating textures using mathematical models. The input to these models can be a set of parameters, random values generated by noise functions, or existing texture images, which may be further processed or combined to generate new textures. Many methods for procedural texture generation have been proposed, but there has been no comprehensive survey or comparison of them yet. In this paper, we present a review of different procedural texture generation methods, according to the characteristics of the generated textures. We divide the different generation methods into two categories: structured texture and unstructured texture generation methods. Example textures are generated using these methods with varying parameter values. Furthermore, we survey post-processing methods based on the filtering and combination of different generation models. We also present a taxonomy of different models, according to the mathematical functions and texture samples they can produce. Finally, a psychophysical experiment is designed to identify the perceptual features of the example textures. Finally, an analysis of the results illustrates the strengths and weaknesses of these methods.

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An Augmented Reality System Using Improved-Iterative Closest Point Algorithm for On-Patient Medical Image Visualization

Cited by 33 publications

References 24 publications

Development of augmented‐reality applications in otolaryngology–head and neck surgery

Development of augmented‐reality applications in otolaryngology–head and neck surgery

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Survey of Procedural Methods for Two-Dimensional Texture Generation

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