Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations

Ye, Menglong; Giannarou, Stamatia; Meining, Alexander; Yang, Guang‐Zhong

doi:10.1016/j.media.2015.10.003

Cited by 48 publications

(24 citation statements)

References 45 publications

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“…To further extract the best location estimates of the tool parts, a consensus-based verification approach [12] is included. This approach analyses the geometrical context of the correspondences in a PROgressive SAmple Consensus (PROSAC) scheme [13].…”

Section: Tool Part Verification Via 2d Geometrical Contextmentioning

confidence: 99%

Real-Time 3D Tracking of Articulated Tools for Robotic Surgery

Zhang

Giannarou

et al. 2016

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

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Abstract. In robotic surgery, tool tracking is important for providing safe tool-tissue interaction and facilitating surgical skills assessment. Despite recent advances in tool tracking, existing approaches are faced with major difficulties in real-time tracking of articulated tools. Most algorithms are tailored for offline processing with pre-recorded videos. In this paper, we propose a real-time 3D tracking method for articulated tools in robotic surgery. The proposed method is based on the CAD model of the tools as well as robot kinematics to generate online part-based templates for efficient 2D matching and 3D pose estimation. A robust verification approach is incorporated to reject outliers in 2D detections, which is then followed by fusing inliers with robot kinematic readings for 3D pose estimation of the tool. The proposed method has been validated with phantom data, as well as ex vivo and in vivo experiments.The results derived clearly demonstrate the performance advantage of the proposed method when compared to the state-of-the-art.

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Section: Tool Part Verification Via 2d Geometrical Contextmentioning

confidence: 99%

Real-Time 3D Tracking of Articulated Tools for Robotic Surgery

Zhang

Giannarou

et al. 2016

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

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“…The video dataset used is taken for research paper and cited [1] .The research work tries to pre-process the endoscopic images using several pre-processing techniques for contrast enhancement and also tries to identify the polyp in the image using Hough transform technique, the contrast enhancement techniques and edge detection technique has less computational complexity and can be applied for real time denoising of endoscopy videos. The Hough transform is not preferred for real time object detection, but based on the assumption of only elliptical polyps in videos, the research work has been carried out to detect the polyps in endoscopic videos.…”

Section: Discussionmentioning

confidence: 99%

Endoscopic Video Pre-Processing using Histogram Equalization and Canny Edge Detection Technique and Hough Transform Technique for Polyp Detection

B S*,

Kavya

2019

IJRTE

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Paper An effective image processing approach has been shown to process real time endoscopic videos to support practitioners in taking vital decisions regarding cancer patients. The images are extracted from real time endoscopy videos using the available software and fed into Matlab for image processing, the results of the processed videos are returned back to the host software. Image processing techniques are used to recognize and enhance the visualization of the polyps present in gastro intestinal tract which help the practitioners in decision making. The intention of the system is to assist the physician or medical practitioner for better visualization and identifying abnormal structures like polyps and bleeding regions during the endoscopic procedures. This proposed system is experimented on the recorded gastrointestinal dataset which contains ten sequence videos with 7894 total frames.

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“…The dataset [40] consists of multiple monocular and stereo medical video sequences which are widely used for validating a variety of applications such as Shape-from-Shading [67], surface reconstruction [26,34], deformable surface tracking [49,72,73] and SLAM [33,41,64]. For all sequences, the dataset maintainers provide high-quality intrinsic and extrinsic calibration information, estimated in the laboratory using a checkerboard helper object.…”

Section: Methodsmentioning

confidence: 99%

Wide baseline pose estimation from video with a density-based uncertainty model

Pellicanò

Aldea

Hégarat‐Mascle

2019

Machine Vision and Applications

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Robust wide baseline pose estimation is an essential step in the deployment of smart camera networks. In this work, we highlight some current limitations of conventional strategies for relative pose estimation in difficult urban scenes. Then, we propose a solution which relies on an adaptive search of corresponding interest points in synchronized video streams which allows us to converge robustly toward a high-quality solution. The core idea of our algorithm is to build across the image space a nonstationary mapping of the local pose estimation uncertainty, based on the spatial distribution of interest points. Subsequently, the mapping guides the selection of new observations from the video stream in order to prioritize the coverage of areas of high uncertainty. With an additional step in the initial stage, the proposed algorithm may also be used for refining an existing pose estimation based on the video data; this mode allows for performing a data-driven self-calibration task for stereo rigs for which accuracy is critical, such as onboard medical or vehicular systems. We validate our method on three different datasets which cover typical scenarios in pose estimation. The results show a fast and robust convergence of the solution, with a significant improvement, compared to single image-based alternatives, of the RMSE of ground-truth matches, and of the maximum absolute error.

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Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations

Cited by 48 publications

References 45 publications

Real-Time 3D Tracking of Articulated Tools for Robotic Surgery

Real-Time 3D Tracking of Articulated Tools for Robotic Surgery

Endoscopic Video Pre-Processing using Histogram Equalization and Canny Edge Detection Technique and Hough Transform Technique for Polyp Detection

Wide baseline pose estimation from video with a density-based uncertainty model

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