Automatic detection of informative frames from wireless capsule endoscopy images

Bashar, Md. Khayrul; Kitasaka, Takayuki; Suenaga, Yasuhito; Mekada, Yoshito; Mori, Kensaku

doi:10.1016/j.media.2009.12.001

Cited by 70 publications

(32 citation statements)

References 19 publications

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“…First, sometimes uninformative images are wrongly detected, which results in incorrect embedding in manifolds; e.g., an image with bubbles can never find a correct correspondence in eigenspaces. In the future, we will improve uninformative frame detection by the work of Atasoy et al [7] or the methods presented in [8,9]. Next, intrabronchus images are wrongly classified.…”

Section: Resultsmentioning

confidence: 99%

ManiSMC: A New Method Using Manifold Modeling and Sequential Monte Carlo Sampler for Boosting Navigated Bronchoscopy

Luo

Kitasaka

Mori

2011

Lecture Notes in Computer Science

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Abstract. This paper presents a new bronchoscope motion tracking method that utilizes manifold modeling and sequential Monte Carlo (SMC) sampler to boost navigated bronchoscopy. Our strategy to estimate the bronchoscope motions comprises two main stages:(1) bronchoscopic scene identification and (2) SMC sampling. We extend a spatial local and global regressive mapping (LGRM) method to Spatial-LGRM to learn bronchoscopic video sequences and construct their manifolds. By these manifolds, we can classify bronchoscopic scenes to bronchial branches where a bronchoscope is located. Next, we employ a SMC sampler based on a selective image similarity measure to integrate estimates of stage (1) to refine positions and orientations of a bronchoscope. Our proposed method was validated on patient datasets. Experimental results demonstrate the effectiveness and robustness of our method for bronchoscopic navigation without an additional position sensor.

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Section: Resultsmentioning

confidence: 99%

ManiSMC: A New Method Using Manifold Modeling and Sequential Monte Carlo Sampler for Boosting Navigated Bronchoscopy

Luo

Kitasaka

Mori

2011

Lecture Notes in Computer Science

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“…According to their studies, 23 % of all images can be discarded, meaning that the visualization time for the manual diagnostic assessment as well as the processing time for automatic diagnostic support can be considerably reduced. In [13], a similar approach is proposed that uses a Gauss Laguerre transform (GLT)-based multiresolution texture feature and introduces a second step that uses spatial segmentation of the bubble region to classify ambiguous frames.…”

Section: Frame Filteringmentioning

confidence: 99%

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

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

114

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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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“…Recent software developments are moving towards computer-aided diagnostic systems, aiming to increase diagnostic yield, reduce inter-observer variability and ultimately make the process of capsule endoscopy reading more efficient (74). Some of these have been trialled in other forms of endoscopy, including upper GI tract endoscopy for Barrett's oesophagus (75), and small-bowel capsule endoscopy (76)(77)(78)(79). A small number of studies have been published detailing the feasibility of automated polyp detection software in CCE (80,81), but much further development is required.…”

Section: Software Developmentsmentioning

confidence: 99%

Review: capsule colonoscopy—a concise clinical overview of current status

Yung¹,

Rondonotti²,

Koulaouzidis³

2016

Ann. Transl. Med.

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Abstract:The colon capsule endoscopy (CCE) was first introduced in 2007. Currently, the main clinical indications for CCE are completion of incomplete colonoscopy, polyp detection and investigation of inflammatory bowel disease (IBD). Although conventional colonoscopy is the gold standard in bowel cancer screening, incomplete colonoscopy remains a problem as lesions are missed. CCE compares favourably to computer tomography colonography (CTC) in adenoma detection and has therefore been proposed as a method for completing colonoscopy. However the data on CCE remains sparse and current evidence does not show its superiority over CTC or conventional colonoscopy in bowel cancer screening. CCE also seems to show good correlation with conventional colonoscopy when used to evaluate IBD, but there are not many published studies at present. Other significant limitations include the need for aggressive bowel preparation and the labour-intensiveness of CCE reading. Therefore, much further software and hardware development is required to enable CCE to fulfill its potential as a minimally-invasive and reliable method of colonoscopy.

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Automatic detection of informative frames from wireless capsule endoscopy images

Cited by 70 publications

References 19 publications

ManiSMC: A New Method Using Manifold Modeling and Sequential Monte Carlo Sampler for Boosting Navigated Bronchoscopy

ManiSMC: A New Method Using Manifold Modeling and Sequential Monte Carlo Sampler for Boosting Navigated Bronchoscopy

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

Review: capsule colonoscopy—a concise clinical overview of current status

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