Image quality assessment of endoscopic panorama images

Behrens, Axel; Bommes, Michael; Groß, Sebastian; Aach, Til

doi:10.1109/icip.2011.6116325

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…To quantify the quality of the final mosaic in terms of the preservation of image structure, Behrens et al [14] have presented a measure based on the structural similarity index (SSIM) published by Wang et al [157] and extended by Li et al [89]. Weibel [161] used a measure called "difference of mean gradient magnitude" (DMGM) to compare the gradient strength within the input images to the gradient strength in the corresponding mosaic regions.…”

Section: B Seam and Structure Qualitymentioning

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: B Seam and Structure Qualitymentioning

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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“…To estimate the quality of texture blending, we adopt a fidelity score method [45] for panoramic images to assess the results. The method estimates panoramic view's preserved quality compared with the input images, and the evaluation is likely consistent with human visual inspection.…”

Section: In Vivo Data Experimentsmentioning

confidence: 99%

Global and Local Panoramic Views for Gastroscopy: An Assisted Method of Gastroscopic Lesion Surveillance

Liu

Wang

Sun

et al. 2015

IEEE Trans. Biomed. Eng.

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Gastroscopy plays an important role in the diagnosis of gastric disease. In this paper, we develop an image panoramic system to assist endoscopists in improving lesion surveillance and reducing many of the tedious operations associated with gastroscopy. The constructed panoramic view has two categories: 1) the local view broadens the endoscopist's field of view in real time. Combining with the original gastroscopic video, this mosaicking view enables the endoscopist to diagnose the lesion comprehensively; 2) the global view constructs a large-area panoramic scene of the internal gastric surface, which can be used for intraoperative surgical navigation and postoperative scene review. Due to the irregular texture and inconsistent reflection of the gastric internal surface, common registration methods cannot accurately stitch this surface. Thereby, a six degree of freedom position tracking endoscope is devised to accommodate for the accumulated mosaicking error and provide efficient mosaicking results. For the global view, a dual-cube constraint model and a Bundle Adjustment algorithm are incorporated to deal with the mosaicking error caused by the irregular inflation and nonrigid deformation of the stomach. Moreover, texture blending and frame selection schemes are developed to make the mosaicking results feasible in real-clinical applications. The experimental results demonstrate that our system performs with a speed of 7.12 frames/s in a standard computer environment, and the mosaicking mean error is 0.43 mm for local panoramic view and 3.71 mm for global panoramic view.

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“…With the profound influence of machine learning, particularly deep learning, in various fields, image quality assessment in endoscopy is undergoing continuous innovation. Alexander et al developed a new fidelity score for quantitative image quality assessment based on the structural similarity maps adopted in the human visual system (HVS), where the measure indicated the extent to which the structural information of relevant structures was preserved in the panorama [10]. Aubreville et al [11] proposed an improved version of the Inception V3 network for detecting motion artifacts in endoscopic images.…”

Section: Introductionmentioning

confidence: 99%

Detection of Image Artifacts Using Improved Cascade Region-Based CNN for Quality Assessment of Endoscopic Images

Sun,

Li,

Liang

et al. 2023

Bioengineering

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Endoscopy is a commonly used clinical method for gastrointestinal disorders. However, the complexity of the gastrointestinal environment can lead to artifacts. Consequently, the artifacts affect the visual perception of images captured during endoscopic examinations. Existing methods to assess image quality with no reference display limitations: some are artifact-specific, while others are poorly interpretable. This study presents an improved cascade region-based convolutional neural network (CNN) for detecting gastrointestinal artifacts to quantitatively assess the quality of endoscopic images. This method detects eight artifacts in endoscopic images and provides their localization, classification, and confidence scores; these scores represent image quality assessment results. The artifact detection component of this method enhances the feature pyramid structure, incorporates the channel attention mechanism into the feature extraction process, and combines shallow and deep features to improve the utilization of spatial information. The detection results are further used for image quality assessment. Experimental results using white light imaging, narrow-band imaging, and iodine-stained images demonstrate that the proposed artifact detection method achieved the highest average precision (62.4% at a 50% IOU threshold). Compared to the typical networks, the accuracy of this algorithm is improved. Furthermore, three clinicians validated that the proposed image quality assessment method based on the object detection of endoscopy artifacts achieves a correlation coefficient of 60.71%.

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Image quality assessment of endoscopic panorama images

Cited by 8 publications

References 9 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

Global and Local Panoramic Views for Gastroscopy: An Assisted Method of Gastroscopic Lesion Surveillance

Detection of Image Artifacts Using Improved Cascade Region-Based CNN for Quality Assessment of Endoscopic Images

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