Vision-based endoscope tracking for 3D ultrasound image-guided surgical navigation

Yang, L.; Wang, Junchen; Ando, Takehiro; Kubota, Akira; Yamashita, Hiromasa; Sakuma, Ichiro; Chiba, Tadatoshi; Kobayashi, Etsuko

doi:10.1016/j.compmedimag.2014.09.003

Cited by 25 publications

(18 citation statements)

References 36 publications

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“…Increasing number of practical utilizations, such as vision based robotics control [58,71,80,108,111,122], surveillance [3,21,25,38,41,89,94,125], navigation [27,32,46,53,70,90,100,121], and electronics in consumer related fields [9,40,49,65,84,113,119], provide applied scenarios for the vision based computation. Most of these applications can be technically attributed into several typical technologies with respect to vision based computation.…”

Section: Introductionmentioning

confidence: 99%

Extensions of principle component analysis with applications on vision based computing

Liu¹,

Kavakli²

2015

Multimed Tools Appl

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This paper mainly focuses on the principle component analysis (PCA) and its applications on vision based computing. The underlying mechanism of PCA given and several significant factors, involved with subspace training are discussed theoretically in detail including principle components energy, residuals assessment, and decomposition computation. The typical extensions, including probabilistic PCA (PPCA), kernel PCA (KPCA), multi-dimensional PCA and robust PCA (RPCA), have been presented with critical analysis on both mechanisms and computations. Combining with the studies on, such as, image compression, visual tracking, image recognition and super-resolution image reconstruction, PCA and its extensions applied to computer vision are critically reviewed and evaluated on the targeted issues in each application as well as the role they played at specific tasks to the characteristics, cost and suitable situations of each PCA based vision processing technique.

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Section: Introductionmentioning

confidence: 99%

Extensions of principle component analysis with applications on vision based computing

Liu¹,

Kavakli²

2015

Multimed Tools Appl

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“…However, in most implementations, the acquired data are processed after the acquisition and cannot be directly correlated with the sample, delaying the diagnostic and analytical result. Recently, various imaging modalities, such as computed tomography, magnetic resonance imaging, ultrasound image and fluorescence lifetime have been combining their respective imaging information with images of the sample in an augmented way to improve or supplement real‐time surgery . These approaches offer the surgeons a direct interpretation of physical information from the tissues by superimposing the imaging modalities information into the visual field of view, allowing the surgeon to detect and determine locations of disease in a real‐time.…”

Section: Introductionmentioning

confidence: 99%

Raman ChemLighter: Fiber optic Raman probe imaging in combination with augmented chemical reality

Yang

Mondol

Stiebing

et al. 2019

Journal of Biophotonics

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Raman spectroscopy using fiber optic probe combines non‐contacted and label‐free molecular fingerprinting with high mechanical flexibility for biomedical, clinical and industrial applications. Inherently, fiber optic Raman probes provide information from a single point only, and the acquisition of images is not straightforward. For many applications, it is highly crucial to determine the molecular distribution and provide imaging information of the sample. Here, we propose an approach for Raman imaging using a handheld fiber optic probe, which is built around computer vision–based assessment of positional information and simultaneous acquisition of spectroscopic information. By combining this implementation with real‐time data processing and analysis, it is possible to create not only fiber‐based Raman imaging but also an augmented chemical reality image of the molecular distribution of the sample surface in real‐time. We experimentally demonstrated that using our approach, it is possible to determine and to distinguish borders of different bimolecular compounds in a short time. Because the method can be transferred to other optical probes and other spectroscopic techniques, it is expected that the implementation will have a large impact for clinical, biomedical and industrial applications.

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“…They range from localization of robots and object manipulation (Choi andChristensen, 2012, Collet et al, 2009), to augmented reality (Müller et al, 2013) implementations running on mobile devices and having only limited resources, thus focusing on fast solutions. Especially in industry, surveying or medical environments, involving machine vision, (close-range) photogrammetry (Luhmann et al, 2006), point-cloud registration (Weinmann et al, 2011) and surgical navigation (Yang et al, 2015), methods are demanded, that are not only robust, but also return a measure of reliability.…”

Section: Introductionmentioning

confidence: 99%

MLPNP – A Real-Time Maximum Likelihood Solution to the Perspective-N-Point Problem

Urban

Leitloff

Hinz

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, a statistically optimal solution to the Perspective-n-Point (PnP) problem is presented. Many solutions to the PnP problem are geometrically optimal, but do not consider the uncertainties of the observations. In addition, it would be desirable to have an internal estimation of the accuracy of the estimated rotation and translation parameters of the camera pose. Thus, we propose a novel maximum likelihood solution to the PnP problem, that incorporates image observation uncertainties and remains real-time capable at the same time. Further, the presented method is general, as is works with 3D direction vectors instead of 2D image points and is thus able to cope with arbitrary central camera models. This is achieved by projecting (and thus reducing) the covariance matrices of the observations to the corresponding vector tangent space.

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Vision-based endoscope tracking for 3D ultrasound image-guided surgical navigation

Cited by 25 publications

References 36 publications

Extensions of principle component analysis with applications on vision based computing

Extensions of principle component analysis with applications on vision based computing

Raman ChemLighter: Fiber optic Raman probe imaging in combination with augmented chemical reality

MLPNP – A Real-Time Maximum Likelihood Solution to the Perspective-N-Point Problem

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