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Stereoscopic static depth perception of enclosed 3D objects
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.ORCID iD: 0000-0002-7750-1917
2013 (English)In: SAP '13 Proceedings of the ACM Symposium on Applied Perception, New York, USA: Association for Computing Machinery (ACM), 2013, 15-22 p.Conference paper (Refereed)
Abstract [en]

Depth perception of semi-transparent virtual objects and the visu-alization of their spatial layout are crucial in many applications, in particular medical applications. Depth cues for opaque objects have been extensively studied, but this is not the case for stereo-scopic semi-transparent objects, in particular in the case when one 3D object is enclosed within a larger exterior object.

In this work we explored different stereoscopic rendering methodsto analyze their impact on depth perception accuracy of an enclosed3D object. Two experiments were performed: the first tested the hypotheses that depth perception is dependent on the color blending of objects (opacity - alpha) for each rendering method and that one of two rendering methods used is superior. The second experiment was performed to corroborate the results of the first experiment and to test an extra hypothesis: is depth perception improved if an auxiliary object that provides a relationship between the enclosed objectand the exterior is used?

The first rendering method used is simple alpha blending with Blinn-Phong shading model, where a segmented brain (exterior object) and a synthetic tumor (enclosed object) were blended. The second rendering method also uses Blinn-Phong, but the shading was modified to preserve silhouettes and to provide an illustrative rendering. Comparing both rendering methods, the brighter regionsof the first rendering method will become more transparent in the second rendering method, thus preserving silhouette areas.

The results show that depth perception accuracy of an enclosed object rendered with a stereoscopic system is dependent on opacity for some rendering methods (simple alpha blending), but this effect is less pronounced than the dependence on object position in relation to the exterior object. The illustrative rendering method is less dependent on opacity. The different rendering methods also perform slightly differently; an illustrative rendering method was superior and the use of an auxiliary object seems to facilitate depth perception.

Place, publisher, year, edition, pages
New York, USA: Association for Computing Machinery (ACM), 2013. 15-22 p.
Keyword [en]
depth perception, stereoscopy, enclosed 3D objects
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-106896DOI: 10.1145/2492494.2492501ISBN: 978-1-4503-2262-1OAI: diva2:719327
2013 ACM Symposium on Applied Perception, SAP 2013; Dublin, Ireland, August 22 - 23, 2013
Available from: 2014-05-23 Created: 2014-05-23 Last updated: 2016-09-15Bibliographically approved
In thesis
1. Guidance and Visualization for Brain Tumor Surgery
Open this publication in new window or tab >>Guidance and Visualization for Brain Tumor Surgery
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Image guidance and visualization play an important role in modern surgery to help surgeons perform their surgical procedures. Here, the focus is on neurosurgery applications, in particular brain tumor surgery where a craniotomy (opening of the skull) is performed to access directly the brain region to be treated. In this type of surgery, once the skull is opened the brain can change its shape, and this deformation is known as brain shift. Moreover, the boundaries of many types of tumors are difficult to identify by the naked eye from healthy tissue. The main goal of this work was to study and develop image guidance and visualization methods for tumor surgery in order to overcome the problems faced in this type of surgery.

Due to brain shift the magnetic resonance dataset acquired before the operation (preoperatively) no longer corresponds to the anatomy of the patient during the operation (intraoperatively). For this reason, in this work methods were studied and developed to compensate for this deformation. To guide the deformation methods, information of the superficial vessel centerlines of the brain was used. A method for accurate (approximately 1 mm) reconstruction of the vessel centerlines using a multiview camera system was developed. It uses geometrical constraints, relaxation labeling, thin plate spline filtering and finally mean shift to find the correct correspondences between the camera images.

A complete non-rigid deformation pipeline was initially proposed and evaluated with an animal model. From these experiments it was observed that although the traditional non-rigid registration methods (in our case coherent point drift) were able to produce satisfactory vessel correspondences between preoperative and intraoperative vessels, in some specific areas the results were suboptimal. For this reason a new method was proposed that combined the coherent point drift and thin plate spline semilandmarks. This combination resulted in an accurate (below 1 mm) non-rigid registration method, evaluated with simulated data where artificial deformations were performed.

Besides the non-rigid registration methods, a new rigid registration method to obtain the rigid transformation between the magnetic resonance dataset and the neuronavigation coordinate systems was also developed.

Once the rigid transformation and the vessel correspondences are known, the thin plate spline can be used to perform the brain shift deformation. To do so, we have used two approaches: a direct and an indirect. With the direct approach, an image is created that represents the deformed data, and with the indirect approach, a new volume is first constructed and only after that can the deformed image be created. A comparison of these two approaches, implemented for the graphics processing units, in terms of performance and image quality, was performed. The indirect method was superior in terms of performance if the sampling along the ray is high, in comparison to the voxel grid, while the direct was superior otherwise. The image quality analysis seemed to indicate that the direct method is superior.

Furthermore, visualization studies were performed to understand how different rendering methods and parameters influence the perception of the spatial position of enclosed objects (typical situation of a tumor enclosed in the brain). To test these methods a new single-monitor-mirror stereoscopic display was constructed. Using this display, stereo images simulating a tumor inside the brain were presented to the users with two rendering methods (illustrative rendering and simple alpha blending) and different levels of opacity. For the simple alpha blending method an optimal opacity level was found, while for the illustrative rendering method all the opacity levels used seemed to perform similarly.

In conclusion, this work developed and evaluated 3D reconstruction, registration (rigid and non-rigid) and deformation methods with the purpose of minimizing the brain shift problem. Stereoscopic perception of the spatial position of enclosed objects was also studied using different rendering methods and parameter values.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. 70 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1762
National Category
Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging Surgery Computer Engineering
urn:nbn:se:liu:diva-130791 (URN)10.3384/diss.diva-130791 (DOI)9789176857724 (Print) (ISBN)
Public defence
2016-09-30, Hugo Theorellsallen (norra entrén), Campus US, Linköping, 09:00 (English)
Swedish Foundation for Strategic Research Knowledge FoundationVINNOVAVårdal Foundation, 2009/0079Swedish Childhood Cancer Foundation, MT2013-0036
Available from: 2016-08-24 Created: 2016-08-24 Last updated: 2016-09-09Bibliographically approved

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Maria Marreiros, Filipe MiguelSmedby, Örjan
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Center for Medical Image Science and Visualization (CMIV)Media and Information TechnologyThe Institute of TechnologyDivision of Radiological SciencesFaculty of Health SciencesDepartment of Radiology in Linköping
Computer Vision and Robotics (Autonomous Systems)

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