Capsule endoscopy image analysis using texture information from various colour models

Charisis, Vasileios; Hadjileontiadis, Leontios; Liatsos, Christos; Mavrogiannis, Christos; Sergiadis, George D.

doi:10.1016/j.cmpb.2011.10.004

Cited by 45 publications

(28 citation statements)

References 22 publications

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“…17 Texture has been used either as the sole feature or jointly with colour (colour-texture). In the context of lesion detection, the texture features that are estimated from the VCE images include second or higher order statistical measures, 31,44,45 measures estimated from f ractal-based image modelling 46 Figure 1 | Results of state-of-the-art software-based methods for video capsule endoscopy. Blue crosses indicate automatically detected salient pixels and red circles indicate which of these pixels have been assessed as abnormal by the system (true positive detections).…”

Section: Lesion Detectionmentioning

confidence: 99%

Software for enhanced video capsule endoscopy: challenges for essential progress

Iakovidis

Koulaouzidis

2015

Nat Rev Gastroenterol Hepatol

187

113

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Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bowel diseases. Furthermore, VCE has the potential to become the leading screening technique for the entire gastrointestinal tract. Computational methods that can be implemented in software can enhance the diagnostic yield of VCE both in terms of efficiency and diagnostic accuracy. Since the appearance of the first capsule endoscope in clinical practice in 2001, information technology (IT) research groups have proposed a variety of such methods, including algorithms for detecting haemorrhage and lesions, reducing the reviewing time, localizing the capsule or lesion, assessing intestinal motility, enhancing the video quality and managing the data. Even though research is prolific (as measured by publication activity), the progress made during the past 5 years can only be considered as marginal with respect to clinically significant outcomes. One thing is clear-parallel pathways of medical and IT scientists exist, each publishing in their own area, but where do these research pathways meet? Could the proposed IT plans have any clinical effect and do clinicians really understand the limitations of VCE software? In this Review, we present an in-depth critical analysis that aims to inspire and align the agendas of the two scientific groups.

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Section: Lesion Detectionmentioning

confidence: 99%

Software for enhanced video capsule endoscopy: challenges for essential progress

Iakovidis

Koulaouzidis

2015

Nat Rev Gastroenterol Hepatol

187

113

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“…Most studies investigate methods for the detection of blood [130][131][132], ulcers [133,140,142], polyps [138][139][140] or tumors in general [136,137]. Fewer studies investigate the detection of CD lesions [134], petechiae [141] and hookworms [143], whereas others aim to discriminate intestinal content, such as bubbles and turbid [144], or as it is more generally expressed, uninformative frames, including also dark parts of the images [145].…”

Section: Concept Nomentioning

confidence: 99%

Optimizing lesion detection in small-bowel capsule endoscopy: from present problems to future solutions

Koulaouzidis

Iakovidis

Karargyris

et al. 2014

Expert Review of Gastroenterology & Hepatology

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This review presents issues pertaining to lesion detection in small-bowel capsule endoscopy (SBCE). The use of prokinetics, chromoendoscopy, diagnostic yield indicators, localization issues and the use of 3D reconstruction are presented. The authors also review the current status (and future expectations) in automatic lesion detection software development. Automatic lesion detection and reporting, and development of an accurate lesion localization system are the main software challenges of our time. The 'smart', selective and judicious use (before as well as during SBCE) of prokinetics in combination with other modalities (such as real time and/or purge) improves the completion rate of SBCE. The tracking of the capsule within the body is important for the localization of abnormal findings and planning of further therapeutic interventions. Currently, localization is based on transit time. Recently proposed software and hardware solutions are proposed herein. Moreover, the feasibility of software-based 3D representation (attempt for 3D reconstruction) is examined.

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“…Nevertheless, color cannot be utilized as a standalone objective modalityfor abnormal tissue detection, since it is not perceptually uniform. Theperceived color ishighly conditioned by the nature and the amount of ambient luminosity [7].Despite the color constancy effect [11] of the human color perception system,whereby the color perception of objects remains relatively constant under varyingenvironmental and visual conditions, serious color variations and color casts exist because ofthe intervention of an endoscope or a camerabetween the intestinal tissue and the physician'seye [11]. For these reasons, gastroenterologists use color and textureinformation together, as thevisual clues, along with other complementary examinations (i.e., blood tests, urine tests etc.…”

Section: Introductionmentioning

confidence: 99%

An optimal IMF selection based on fast BEEMD with Dlac analysis for detection of Polyp and Ulcer in WCE images

Jadhav

Dabhole

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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the main contribution of this paper is the presentation of a novel tool for WCE image analysis and classification by exploiting color-texture features. The proposed scheme has based on the ingenious combination of optimal selection of image components (IMFs) ofBEEMD and DLac, applied on the green/red component of WCE images in order to identify ulcerations and polyp affected images from WCE images.Optimal IMF's of BEEMDwas exploited to reveal the intrinsic components (IMFs) of the images in order to achieve data driven, Coefficient of standard Deviation and efficient SVM classifier to boost the distinctness between polyp and ulcer regions. However, DLac analysis facilitates to extract efficient texturecharacteristics. The proposed approach has evaluated on selected WCE images, captured from patients, depicting ulcer and polyp tissue.The optimum image components (IMFs) that contain the majority of texture information include IMFs 5-8which produce 100% accuracy for ulcer images. Individual IMFs score up to 80 % classification accuracy, while their higher exploitation as a group enhances the detection rate up to 93.34% for ulcer and 90% for polyp tissue.

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Capsule endoscopy image analysis using texture information from various colour models

Cited by 45 publications

References 22 publications

Software for enhanced video capsule endoscopy: challenges for essential progress

Software for enhanced video capsule endoscopy: challenges for essential progress

Optimizing lesion detection in small-bowel capsule endoscopy: from present problems to future solutions

An optimal IMF selection based on fast BEEMD with Dlac analysis for detection of Polyp and Ulcer in WCE images

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