A graph-based approach for local and global panorama imaging in cystoscopy

Bergen, Tobias; Wittenberg, Thomas; Münzenmayer, Christian; Chen, Grace; Hager, Gregory D.

doi:10.1117/12.2008174

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…They explored the creation of panorama images from cystoscopies with pure image-processing methods, as well as navigation support using inertial sensors. Bergen et al [24] (2013) have applied a graph-based approach to stitch images from cystoscopic video. They identified coherent submaps from the frame-graph to stitch local patches which are combined to a larger mosaic afterwards.…”

Section: Approachesmentioning

confidence: 99%

Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

Bergen

Wittenberg

2016

IEEE J. Biomed. Health Inform.

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Endoscopic procedures form part of routine clinical practice for minimally invasive examinations and interventions. While they are beneficial for the patient, reducing surgical trauma and making convalescence times shorter, they make orientation and manipulation more challenging for the physician, due to the limited field of view through the endoscope. However, this drawback can be reduced by means of medical image processing and computer vision, using image stitching and surface reconstruction methods to expand the field of view. This paper provides a comprehensive overview of the current state of the art in endoscopic image stitching and surface reconstruction. The literature in the relevant fields of application and algorithmic approaches is surveyed. The technological maturity of the methods and current challenges and trends are analyzed.

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Section: Approachesmentioning

confidence: 99%

Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

Bergen

Wittenberg

2016

IEEE J. Biomed. Health Inform.

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“…Aside from differences in topological features, the airway tree produces video of similar color content from anywhere within the lungs. 1,7 For our problem, we introduce the concept of an endoscopic shot, which is motivated by the sub-map organization of cystoscopy video sequences presented by Bergen et al 9 Additionally, we also incorporate ideas proposed for laparoscopic frame analysis by Soper et al 10 To begin endoscopic-shot segmentation, speeded-up robust features (SURF) are extracted from each endobronchial video frame. An initial endoscopic shot S 0 is created that includes the first video frame I V (0); i.e., S i = {j}, where i = 0 is the shot index and j = 0 indicates the index of the included video frame.…”

Section: Endoscopic-shot Segmentationmentioning

confidence: 99%

Method for endobronchial video parsing

Byrnes

Higgins

2016

SPIE Proceedings

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Endoscopic examination of the lungs during bronchoscopy produces a considerable amount of endobronchial video. A physician uses the video stream as a guide to navigate the airway tree for various purposes such as general airway examinations, collecting tissue samples, or administering disease treatment. Aside from its intraoperative utility, the recorded video provides high-resolution detail of the airway mucosal surfaces and a record of the endoscopic procedure. Unfortunately, due to a lack of robust automatic video-analysis methods to summarize this immense data source, it is essentially discarded after the procedure. To address this problem, we present a fully-automatic method for parsing endobronchial video for the purpose of summarization. Endoscopicshot segmentation is first performed to parse the video sequence into structurally similar groups according to a geometric model. Bronchoscope-motion analysis then identifies motion sequences performed during bronchoscopy and extracts relevant information. Finally, representative key frames are selected based on the derived motion information to present a drastically reduced summary of the processed video. The potential of our method is demonstrated on four endobronchial video sequences from both phantom and human data. Preliminary tests show that, on average, our method reduces the number of frames required to represent an input video sequence by approximately 96% and consistently selects salient key frames appropriately distributed throughout the video sequence, enabling quick and accurate post-operative review of the endoscopic examination.

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“…Our method, which builds upon the work of Soper et al and Bergen et al for frame reduction and sub-map generation, first parses an input video stream by removing redundant or uninformative frames and then organizes positionally related frames into sub-maps. 22,26 Next, we use manual interaction to select key frames from the collection of sub-maps. It is important to note that all bronchoscopic video frames are corrected for barrel distortion prior to any video processing and to also facilitate proper CT-video registration later.…”

Section: Video Analysismentioning

confidence: 99%

“…[19][20][21][22][23] Additional research focused on creating wide field-of-view panoramic images from positionallyrelated collections of endoscopic video frames. 22,[24][25][26] Unfortunately, in the realm of bronchoscopy, no method exists for effectively exploiting the considerable information available in bronchoscopic video streams and for subsequent linkage of this information to a patient's 3D MDCT chest scan. Also, in order to effectively exploit the information in large video streams, we believe suitable automated data analysis is required.…”

Section: Introductionmentioning

confidence: 99%

Construction of a multimodal CT-video chest model

Byrnes

Higgins

2014

SPIE Proceedings

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Bronchoscopy enables a number of minimally invasive chest procedures for diseases such as lung cancer and asthma. For example, using the bronchoscope's continuous video stream as a guide, a physician can navigate through the lung airways to examine general airway health, collect tissue samples, or administer a disease treatment. In addition, physicians can now use new image-guided intervention (IGI) systems, which draw upon both three-dimensional (3D) multi-detector computed tomography (MDCT) chest scans and bronchoscopic video, to assist with bronchoscope navigation. Unfortunately, little use is made of the acquired video stream, a potentially invaluable source of information. In addition, little effort has been made to link the bronchoscopic video stream to the detailed anatomical information given by a patient's 3D MDCT chest scan. We propose a method for constructing a multimodal CT-video model of the chest. After automatically computing a patient's 3D MDCT-based airway-tree model, the method next parses the available video data to generate a positional linkage between a sparse set of key video frames and airway path locations. Next, a fusion/mapping of the video's color mucosal information and MDCT-based endoluminal surfaces is performed. This results in the final multimodal CT-video chest model. The data structure constituting the model provides a history of those airway locations visited during bronchoscopy. It also provides for quick visual access to relevant sections of the airway wall by condensing large portions of endoscopic video into representative frames containing important structural and textural information. When examined with a set of interactive visualization tools, the resulting fused data structure provides a rich multimodal data source. We demonstrate the potential of the multimodal model with both phantom and human data.

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A graph-based approach for local and global panorama imaging in cystoscopy

Cited by 13 publications

References 13 publications

Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

Method for endobronchial video parsing

Construction of a multimodal CT-video chest model

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