Particle filtering in the Hough space for instrument tracking

Climent, Joan Mahiques; Hexsel, Roberto A.

doi:10.1016/j.compbiomed.2012.02.007

Cited by 16 publications

(5 citation statements)

References 26 publications

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“…Gradients are another very popular feature. Typically, the Hough transform [72,73] and histogram of oriented gradients (HOG) descriptors [74] are widely used to extract the edges of surgical tools. These gradient features can extract the edges and corners of surgical tools well, but noise easily interferes with them.…”

Section: Figure 5 Example Of Hand-eye Calibrationmentioning

confidence: 99%

The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology

Jin

Zhang

et al. 2021

IEEE Access

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Minimally invasive medicine has become mainstream because of its crucial clinical significance in providing a low risk of postoperative complications, limited blood loss, short postoperative recovery time, and small sizes of associated physiological tissue wounds. Endoscopic navigation systems comprise a research hot spot in medical science and technology and are an essential means to achieve precision medicine and improve surgical operation safety. As a core component in endoscopic navigation during minimally invasive surgery, endoscopes play a critical role in disease diagnosis and treatment. The development of endoscopic vision technologies has resulted in a renewed drive to further develop endoscopic navigation systems. Multiple endoscopic optical imaging modalities provide data sources for endoscopic vision technology, including white-light endoscopy, contrast-enhanced imaging and technologies involving magnified observation. Endoscopic vision is a specific application of computer vision involving the use of endoscopes that include instrument tracking, endoscopic view expansion, and suspicious lesion tracking in the application of endoscopic navigation. These techniques help surgeons or surgical robots locate instruments and lesions and expand the field of view of the endoscope. Although these technologies have been applied to various clinical and pre-clinical diagnoses and treatments, the use and combination of these advanced technologies in endoscopic navigation system for specific clinical requirements remains challenging. This review performs a broad survey of advanced endoscopic vision technologies and their application in endoscopic navigation systems. Finally, we discuss the challenges and future directions in implementing and developing endoscopic navigation systems.

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Section: Figure 5 Example Of Hand-eye Calibrationmentioning

confidence: 99%

The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology

Jin

Zhang

et al. 2021

IEEE Access

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“…Alexiev extended the application range of Hough transform from the single receiver to multiple receiver scenarios, and analysed the influence of Gaussian noise on the probability of track detection using Hough transform with Monte Carlo simulation [19]. Taking advantage of the robust property of Hough transform, Climent et al proposed a fuzzy Hough transform used in complex scenarios to replace the binary cumulative detection function of the traditional objective function of Hough transform [20]. Compared with mean-shift tracker, Fan et al proposed joining Hough transform, chaos theory and uncertainty theory to promote the real target detection rate and decrease the false track numbers [21].…”

Section: Introductionmentioning

confidence: 99%

“…Taking advantage of the robust property of Hough transform, Climent et al. proposed a fuzzy Hough transform used in complex scenarios to replace the binary cumulative detection function of the traditional objective function of Hough transform [20]. Compared with mean‐shift tracker, Fan et al.…”

Section: Introductionmentioning

confidence: 99%

Prior knowledge‐based statistical estimation of linear false tracks in uniform distributed clutter

Wang

Shi

et al. 2021

IET Radar Sonar & Navi

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A large number of initialised false tracks in clutter environments will cause a heavy burden on the detection and track-processing system. The quantity of initialised false tracks in clutter used to be estimated by experience or simulation, which lacks a fundamental explanation in theory. In order to estimate the amount of linear initialised false tracks with analytical expressions, statistical estimators are proposed based on prior knowledge of the clutter in a two-dimensional scenario. Meanwhile, the differential Hough transform is presented to verify the estimators. To be specific, firstly, a one-dimensional scenario is illustrated to present the statistical estimators of the number of initialised linear and equallength false tracks, which is based on statistical estimation theory. Secondly, regarding the clutter map as a uniform distributed binary image, the estimators described at first are extended to a two-dimensional situation. Finally, to verify the validity of the proposed estimators, a differential Hough transform algorithm is utilised to obtain the number of linear and equal-length tracks in the simulation. The results show that the estimators proposed herein are reasonable in a certain clutter environment.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Voros et al [256] define a cylindrical shape model and use geometric information to detect the edges of the tool shaft and the symmetry axis. A similar approach using the Hough transform to detect the straight lines of the instrument shaft is presented in [41]. These approaches face a number of challenges like homogenous color distribution, indistinct edges, occlusions, blurriness, specular reflections and artifacts like smoke, blood or liquids.…”

Section: Instrument Detection and Trackingmentioning

confidence: 99%

Content-based processing and analysis of endoscopic images and videos: A survey

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

112

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In recent years, digital endoscopy has established as key technology for medical screenings and minimally invasive surgery. Since then, various research communities with manifold backgrounds have picked up on the idea of processing and automatically analyzing the inherently available video signal that is produced by the endoscopic camera. Proposed works mainly include image processing techniques, pattern recognition, machine learning methods and Computer Vision algorithms. While most contributions deal with realtime assistance at procedure time, the post-procedural processing of recorded videos is still in its infancy. Many post-processing problems are based on typical Multimedia methods like indexing, retrieval, summarization and video interaction, but have only been sparsely addressed so far for this domain. The goals of this survey are (1) to introduce this research field to a broader audience in the Multimedia community to stimulate further research, (2) to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and (3) to systematically bring together the very diverse research results for the first time to provide a broader overview of related research that is currently not perceived as belonging together.

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Particle filtering in the Hough space for instrument tracking

Cited by 16 publications

References 26 publications

The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology

The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology

Prior knowledge‐based statistical estimation of linear false tracks in uniform distributed clutter

Content-based processing and analysis of endoscopic images and videos: A survey

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