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Current Medical Imaging

Author(s): Kuntesh Ketan Jani* and Rajeev Srivastava

DOI: 10.2174/1573405614666181102152434

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A Survey on Medical Image Analysis in Capsule Endoscopy

Page: [622 - 636] Pages: 15

  • * (Excluding Mailing and Handling)

Abstract

Background and Objective: Capsule Endoscopy (CE) is a non-invasive, patient-friendly alternative to conventional endoscopy procedure. However, CE produces 6 to 8 hrs long video posing a tedious challenge to a gastroenterologist for abnormality detection. Major challenges to an expert are lengthy videos, need of constant concentration and subjectivity of the abnormality. To address these challenges along with high diagnostic accuracy, design and development of automated abnormality detection system is a must. Machine learning and computer vision techniques are devised to develop such automated systems.

Methods: Study presents a review of quality research papers published in IEEE, Scopus, and Science Direct database with search criteria as capsule endoscopy, engineering, and journal papers. The initial search retrieved 144 publications. After evaluating all articles, 62 publications pertaining to image analysis are selected.

Results: This paper presents a rigorous review comprising all the aspects of medical image analysis concerning capsule endoscopy namely video summarization and redundant image elimination, Image enhancement and interpretation, segmentation and region identification, Computer-aided abnormality detection in capsule endoscopy, Image and video compression. The study provides a comparative analysis of various approaches, experimental setup, performance, strengths, and limitations of the aspects stated above.

Conclusions: The analyzed image analysis techniques for capsule endoscopy have not yet overcome all current challenges mainly due to lack of dataset and complex nature of the gastrointestinal tract.

Keywords: CE, image-analysis, automated abnormality detection, non-invasive, gastroenterologist, medical image analysis.

Graphical Abstract

[1]
Charfi S, El Ansari M. Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images. Multimedia Tools Appl 2018; 77(3): 4047-64.
[http://dx.doi.org/10.1007/s11042-017-4555-7]
[2]
Suman S, Hussin FA, Malik AS, et al. Feature selection and classification of ulcerated lesions using statistical analysis for WCE images. Appl Sci (Basel) 2017; 7(10): 1097.
[http://dx.doi.org/10.3390/app7101097]
[3]
Vu H, Echigo T, Sagawa R, et al. Detection of contractions in adaptive transit time of the small bowel from wireless capsule endoscopy videos. Comput Biol Med 2009; 39(1): 16-26.
[http://dx.doi.org/10.1016/j.compbiomed.2008.10.005]
[4]
CapsuleEndoscope. [cited 2018 Mar 6]. Available from: . https://commons.wikimedia.org/w/index.php?curid=819896
[5]
Stephen J Swift. Reducing size while improving functionality and safety in next-generation medical device design. 2012. [cited 2018 Mar 6
[6]
Li B, Meng MQH. Texture analysis for ulcer detection in capsule endoscopy images. Image Vis Comput 2009; 27(9): 1336-42.
[http://dx.doi.org/10.1016/j.imavis.2008.12.003]
[7]
Liu G, Yan G, Kuang S, Wang Y. Detection of small bowel tumor based on multi-scale curvelet analysis and fractal technology in capsule endoscopy. Comput Biol Med 2016; 70: 131-8.
[http://dx.doi.org/10.1016/j.compbiomed.2016.01.021]
[8]
Vilariño F, Kuncheva LI, Radeva P. ROC curves and video analysis optimization in intestinal capsule endoscopy. Pattern Recognit Lett 2006; 27(8): 875-81.
[http://dx.doi.org/10.1016/j.patrec.2005.10.011]
[9]
Iakovidis DK, Tsevas S, Polydorou A. Reduction of capsule endoscopy reading times by unsupervised image mining. Comput Med Imaging Graph 2010; 34(6): 471-8.
[http://dx.doi.org/10.1016/j.compmedimag.2009.11.005]
[10]
Mehmood I, Sajjad M, Baik SW. Video summarization based tele-endoscopy: A service to efficiently manage visual data generated during wireless capsule endoscopy procedure. J Med Syst 2014; 38(9): 109.
[http://dx.doi.org/10.1007/s10916-014-0109-y]
[11]
Zhao Q, Mullin GE, Meng MQH, Dassopoulos T, Kumar R. A general framework for wireless capsule endoscopy study synopsis. Comput Med Imaging Graph 2015; 41: 108-16.
[http://dx.doi.org/10.1016/j.compmedimag.2014.05.011]
[12]
Lee HG, Choi MK, Shin BS, Lee SC. Reducing redundancy in wireless capsule endoscopy videos. Comput Biol Med 2013; 43(6): 670-82.
[http://dx.doi.org/10.1016/j.compbiomed.2013.02.009]
[13]
Bashar MK, Kitasaka T, Suenaga Y, Mekada Y, Mori K. Automatic detection of informative frames from wireless capsule endoscopy images. Med Image Anal 2010; 14(3): 449-70.
[http://dx.doi.org/10.1016/j.media.2009.12.001]
[14]
Li C, Ben Hamza A, Bouguila N, Wang X, Ming F, Xiao G. Online redundant image elimination and its application to wireless capsule endoscopy. Signal Image Video Process 2012; 8(8): 1497-506.
[http://dx.doi.org/10.1007/s11760-012-0384-3]
[15]
Ben Ismail MM, Bchir O. Endoscopy video summarisation using novel relational motion histogram descriptor and semi-supervised clustering. J Exp Theor Artif Intell 2016; 28(4): 629-53.
[http://dx.doi.org/10.1080/0952813X.2015.1020623]
[16]
Szczypiński PM, Sriram RD, Sriram PVJ, Reddy DN. A model of deformable rings for interpretation of wireless capsule endoscopic videos. Med Image Anal 2009; 13(2): 312-24.
[http://dx.doi.org/10.1016/j.media.2008.12.002]
[17]
Li B, Meng MQH. Wireless capsule endoscopy images enhancement via adaptive contrast diffusion. J Vis Commun Image Represent 2012; 23(1): 222-8.
[http://dx.doi.org/10.1016/j.jvcir.2011.10.002]
[18]
Karargyris A, Bourbakis N. Three-dimensional reconstruction of the digestive wall in capsule endoscopy videos using elastic video interpolation. IEEE Trans Med Imaging 2011; 30(4): 957-71.
[http://dx.doi.org/10.1109/TMI.2010.2098882]
[19]
Mackiewicz M, Berens J, Fisher M. Wireless capsule endoscopy color video segmentation. IEEE Trans Med Imaging 2008; 27(12): 1769-81.
[http://dx.doi.org/10.1109/TMI.2008.926061]
[20]
Singh VP, Srivastava S, Srivastava R. Automated and effective content-based image retrieval for digital mammography. J XRay Sci Technol 2018; 26(1): 29-49.
[http://dx.doi.org/10.3233/XST-17306]
[21]
Masood S, Sharif M, Masood A, Yasmin M, Raza M. A Survey on medical image segmentation. Curr Med Imaging Rev 2015; 11(1): 3-14.
[http://dx.doi.org/10.2174/157340561101150423103441]
[22]
Baâzaoui A, Barhoumi W, Zagrouba E, Mabrouk R. A survey of PET image segmentation: Applications in oncology, cardiology and neurology. Curr Med Imaging Rev 2016; 12(1): 13-27.
[http://dx.doi.org/10.2174/1573405612666151203204003]
[23]
Arivazhagan S, Sylvia Lilly Jebarani W, Jenifer Daisy V. Categorization and segmentation of intestinal content and pathological frames in wireless capsule endoscopy images. Int J Imaging Robot 2014; 13(2): 134-47.
[24]
Shen Y, Guturu PP, Buckles BP. Wireless capsule endoscopy video segmentation using an unsupervised learning approach based on probabilistic latent semantic analysis with scale invariant features. IEEE Trans Inf Technol Biomed 2012; 16(1): 98-105.
[http://dx.doi.org/10.1109/TITB.2011.2171977]
[25]
Yihua L, Xingang Z, Liu Z, L ZhaoL, M Li. Inf Technol J 2013; 12(16): 3815-9.
[http://dx.doi.org/10.3923/itj.2013.3815.3819]
[26]
Chen H, Wu X, Tao G, Peng Q. Automatic content understanding with cascaded spatial–temporal deep framework for capsule endoscopy videos. Neurocomputing 2017; 229: 77-87.
[http://dx.doi.org/10.1016/j.neucom.2016.06.077]
[27]
Kaur P, Singh G, Kaur P. A review of denoising medical images using machine learning approaches. Curr Med Imaging Rev 2017; 13: 675-85.
[28]
Ghosh T, Fattah SA, Wahid KA, Zhu WP, Ahmad MO. Cluster based statistical feature extraction method for automatic bleeding detection in wireless capsule endoscopy video. Comput Biol Med 2017; 2018(94): 41-54.
[http://dx.doi.org/10.1016/j.compbiomed.2017.12.014]
[29]
Singh VP, Srivastava R. Automated and effective content-based mammogram retrieval using wavelet based CS-LBP feature and self-organizing map. Integr Med Res 2017; 38(1): 90-105.
[30]
Yanagawa Y, Echigo T, Vu H, et al. Abnormality tracking during video capsule endoscopy using an affine triangular constraint based on surrounding features. IPSJ Trans Comput Vis Appl 2017; 9(1): 1-10.
[http://dx.doi.org/10.1186/s41074-017-0015-6]
[31]
Kumar R, Srivastava S, Srivastava R. A fourth order PDE based fuzzy c-means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection. Comput Methods Programs Biomed 2017; 146: 59-68.
[http://dx.doi.org/10.1016/j.cmpb.2017.05.003]
[32]
Kodogiannis VS, Boulougoura M, Lygouras JN, Petrounias I. A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images. Neurocomputing 2007; 70(4-6): 704-17.
[http://dx.doi.org/10.1016/j.neucom.2006.10.024]
[33]
Bonnel J, Khademi A, Krishnan S, Ioana C. Small bowel image classification using cross-co-occurrence matrices on wavelet domain. Biomed Signal Process Control 2009; 4(1): 7-15.
[http://dx.doi.org/10.1016/j.bspc.2008.07.002]
[34]
Li B, Meng MQH. Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments. Comput Biol Med 2009; 39(2): 141-7.
[http://dx.doi.org/10.1016/j.compbiomed.2008.11.007]
[35]
Liu J, Yuan X. Obscure bleeding detection in endoscopy images using support vector machines. Optim Eng 2009; 10(2): 289-99.
[http://dx.doi.org/10.1007/s11081-008-9066-y]
[36]
Li B, Meng MQH. Computer-aided detection of bleeding regions for capsule endoscopy images. IEEE Trans Biomed Eng 2009; 56(4): 1032-9.
[http://dx.doi.org/10.1109/TBME.2008.2010526]
[37]
Woo SH, Cho JH. Telemetry system for slow wave measurement from the small bowel. Med Biol Eng Comput 2010; 48(3): 277-83.
[http://dx.doi.org/10.1007/s11517-009-0567-4]
[38]
Vilariño F, Spyridonos P, Deiorio F, Vitria J, Azpiroz F, Radeva P. Intestinal motility assessment with video capsule endoscopy: Automatic annotation of phasic intestinal contractions. IEEE Trans Med Imaging 2010; 29(2): 246-59.
[http://dx.doi.org/10.1109/TMI.2009.2020753]
[39]
Pan GB, Yan GZ, Song XS, Qiu XL. Bleeding detection from wireless capsule endoscopy images using improved euler distance in CIELab. J Shanghai Jiaotong Univ 2010; 15(2): 218-23.
[http://dx.doi.org/10.1007/s12204-010-9716-z]
[40]
Karargyris A, Bourbakis N. Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos. IEEE Trans Biomed Eng 2011; 58(10 PART 1): 2777-86.
[http://dx.doi.org/10.1109/TBME.2011.2155064]
[41]
Li B, Meng MQH, Lau JYW. Computer-aided small bowel tumor detection for capsule endoscopy. Artif Intell Med 2011; 52(1): 11-6. [Internet]
[http://dx.doi.org/10.1016/j.artmed.2011.01.003]
[42]
Pan G, Yan G, Qiu X, Cui J. Bleeding detection in wireless capsule Endoscopy based on probabilistic neural network. J Med Syst 2011; 35(6): 1477-84.
[http://dx.doi.org/10.1007/s10916-009-9424-0]
[43]
Charisis VS, Hadjileontiadis LJ, Liatsos CN, Mavrogiannis CC, Sergiadis GD. Capsule endoscopy image analysis using texture information from various colour models. Comput Methods Programs Biomed 2012; 107(1): 61-74. [Internet]
[http://dx.doi.org/[http://10.1016/j.cmpb.2011.10.004]
[44]
Li B, Meng MQ-H. Automatic polyp detection for wireless capsule endoscopy images. Expert Syst Appl 2012; 39(12): 10952-8.
[http://dx.doi.org/10.1016/j.eswa.2012.03.029]
[45]
Li BP, Meng MQH. Comparison of several texture features for tumor detection in CE images. J Med Syst 2012; 36(4): 2463-9.
[http://dx.doi.org/10.1007/s10916-011-9713-2]
[46]
Li B, Meng MQH. Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection. IEEE Trans Inf Technol Biomed 2012; 16(3): 323-9.
[http://dx.doi.org/10.1109/TITB.2012.2185807]
[47]
Drozdzal M, Segu S, Vitri J, Malagelada C, Azpiroz F, Radeva P. Adaptable image cuts for motility inspection using WCE. Comput Med Imaging Graph [Internet] 2013; 37(1): 72-80.
[48]
Szczypiński P, Klepaczko A, Pazurek M, Daniel P. Texture and color based image segmentation and pathology detection in capsule endoscopy videos. Comput Methods Programs Biomed 2014; 113(1): 396-411.
[http://dx.doi.org/10.1016/j.cmpb.2012.09.004]
[49]
Sainju S, Bui FM, Wahid KA. Automated bleeding detection in capsule endoscopy videos using statistical features and region growing. J Med Syst 2014; 38(4)
[http://dx.doi.org/10.1007/s10916-014-0025-1]
[50]
Nawarathna R, Oh J, Muthukudage J, Tavanapong W, et al. Abnormal image detection in endoscopy videos using a filter bank and local binary patterns. Neurocomputing 2014; 144: 70-91.
[http://dx.doi.org/10.1016/j.neucom.2014.02.064]
[51]
Mamonov AV, Figueiredo IN, Figueiredo PN, Tsai YR. Automated polyp detection in colon capsule endoscopy by automated polyp detection in colon capsule endoscopy. Ices Rep 2013; 33(7): 1-16.
[52]
Hassan AR, Haque MA. Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos. Comput Methods Programs Biomed 2015; 122(3): 341-53.
[http://dx.doi.org/10.1016/j.cmpb.2015.09.005]
[53]
Graca C, Falcao G, Figueiredo IN, Kumar S. Hybrid multi-GPU computing: accelerated kernels for segmentation and object detection with medical image processing applications. J Real-Time Image Process 2017; 13(1): 227-44.
[http://dx.doi.org/10.1007/s11554-015-0517-3]
[54]
Kumar R, Zhao Q, Seshamani S, Mullin G, Hager G, Dassopoulos T. Assessment of crohn’s disease lesions in wireless capsule endoscopy images. IEEE Trans Biomed Eng 2012; 59(2): 355-62.
[http://dx.doi.org/10.1109/TBME.2011.2172438]
[55]
Yuan Y, Wang J, Li B. Meng M q-HH. Saliency based ulcer detection for wireless capsule endoscopy Diagnosis. IEEE Trans Med Imaging 2015; 34(10): 2046-57.
[http://dx.doi.org/10.1109/TMI.2015.2418534]
[56]
Ševo I, Avramović A, Balasingham I, Elle OJ, Bergsland J, Aabakken L. Edge density based automatic detection of inflammation in colonoscopy videos. Comput Biol Med 2016; 72: 138-50.
[http://dx.doi.org/10.1016/j.compbiomed.2016.03.017]
[57]
Usman MA, Satrya GB, Usman MR, Shin SY. Detection of small colon bleeding in wireless capsule endoscopy videos. Comput Med Imaging Graph 2016; 54: 16-26.
[http://dx.doi.org/10.1016/j.compmedimag.2016.09.005]
[58]
Liu DY, Gan T, Rao NN, et al. Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process. Med Image Anal 2016; 32: 281-94.
[http://dx.doi.org/10.1016/j.media.2016.04.007]
[59]
Yuan Y, Li B, Meng MQH. Bleeding frame and region detection in the wireless capsule endoscopy video. IEEE J Biomed Health Inform 2016; 20(2): 624-30.
[http://dx.doi.org/10.1109/JBHI.2015.2399502]
[60]
Yuan Y, Li B, Meng MQH. Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images. IEEE Trans Autom Sci Eng 2016; 13(2): 529-35.
[http://dx.doi.org/10.1109/TASE.2015.2395429]
[61]
Wu X, Chen H, Gan T, Chen J, Ngo CW, Peng Q. Automatic hookworm detection in wireless capsule endoscopy images. IEEE Trans Med Imaging 2016; 35(7): 1741-52.
[http://dx.doi.org/10.1109/TMI.2016.2527736]
[62]
Seguí S, Drozdzal M, Pascual G, et al. Generic feature learning for wireless capsule endoscopy analysis. Comput Biol Med 2016; 79: 163-72.
[http://dx.doi.org/10.1016/j.compbiomed.2016.10.011]
[63]
Ross BWW. Bibliography and abstracts. Med Electron Biol Eng 1964; 2(1): 349-77.
[64]
Singh VP, Srivastava R. Improved image retrieval using fast colour-texture features with varying weighted similarity measure and random forests. Multimed Tools Appl 2017; pp. 1-26.
[65]
Fante KA, Bhaumik B, Chatterjee S. Design and implementation of computationally efficient image compressor for wireless capsule endoscopy. Circuits Syst Signal Process 2016; 35(5): 1677-703.
[http://dx.doi.org/10.1007/s00034-015-0136-z]
[66]
Lin L-H, Chen T-J. Mutual Information Correlation with Human Vision in Medical Image Compression. Curr Med Imaging Rev 2017; 14(1): 64-70.
[http://dx.doi.org/10.2174/1573405613666171003151036]
[67]
Turgis D, Puers R. Image compression in video radio transmission for capsule endoscopy. Sens Actuators A Phys 2005; 123-124: 129-36.
[http://dx.doi.org/10.1016/j.sna.2005.05.016]
[68]
Thoné J, Verlinden J, Puers R. An efficient hardware-optimized compression algorithm for wireless capsule endoscopy image transmission. Procedia Eng 2010; 5: 208-11.
[http://dx.doi.org/10.1016/j.proeng.2010.09.084]
[69]
Khan TH, Wahid KA. Lossless and low-power image compressor for wireless capsule endoscopy. VLSI Des 2011; 2011: 1-12.
[http://dx.doi.org/10.1155/2011/343787]
[70]
Turcza P, Duplaga M. Low power FPGA-based image processing core for wireless capsule endoscopy. Sens Actuators A Phys 2011; 172(2): 552-60.
[http://dx.doi.org/10.1016/j.sna.2011.09.026]
[71]
Khan T, Wahid K. Low power and low complexity compressor for video capsule endoscopy. Circuits Syst Video 2011; 21(10): 1534-46.
[http://dx.doi.org/10.1109/TCSVT.2011.2163985]
[72]
Khan TH, Wahid K. Low-complexity colour-space for capsule endoscopy image compression. Electron Lett 2011; 47(22): 1217.
[http://dx.doi.org/10.1049/el.2011.2211]
[73]
Deligiannis N, Verbist F, Iossifides AC, et al. Wyner-Ziv video coding for wireless lightweight multimedia applications. EURASIP J Wirel Commun Netw 2012; 2012(1): 106.
[http://dx.doi.org/10.1186/1687-1499-2012-106]
[74]
Khan TH, Wahid KA. Subsample-based image compression for capsule endoscopy. J Real-Time Image Process 2013; 8(1): 5-19.
[http://dx.doi.org/10.1007/s11554-011-0208-7]
[75]
Khan TH, Wahid KA. White and narrow band image compressor based on a new color space for capsule endoscopy. Signal Process Image Commun 2014; 29(3): 345-60.
[http://dx.doi.org/10.1016/j.image.2013.12.001]