Formulation and statistical evaluation of an automated algorithm for locating small bowel tumours in wireless capsule endoscopy

• Autorzy: Jagadeesan A. , Sivaraman J.

Identyfikatory

DOI

10.1016/j.bbe.2018.07.003

• Języki publikacji: EN

Abstrakty

EN

Wireless capsule endoscopy (WCE) is an imaging modality which is highly reliable in the diagnosis of small bowel tumors. But locating the frames carrying tumors manually from the lengthy WCE is cumbersome and time consuming. A simple algorithm for the automated detection of tumorous frames from WCE is proposed in this work. In the proposed algorithm, local binary pattern (LBP) of the contrast enhanced green channel is used as the textural descriptor of the WCE frames. The features employed to differentiate tumorous and nontumorous frames are skewness (S) and kurtosis (K) of the LBP histogram. The threshold value of the features which offers the trade-off between sensitivity and specificity is identified through Receiver Operating Characteristic (ROC) curve analysis. At the optimum threshold, both the features exhibited a sensitivity of 100% and specificity of 90%. The skewness and kurtosis of the LBP computed from the enhanced green channel of tumorous and nontumorous frames differ significantly ( p « 0.05) with a p-value of 2.2 x 10^-16. The proposed method is helpful to reduce the time spent by the doctors for reviewing WCE.

Słowa kluczowe

EN

kurtosis; local binary pattern; receiver operating characteristic; small bowel tumor; skewness; wireless capsule endoscopy

PL

kurtoza; lokalne wzorce binarne; guz jelita cienkiego; skośność; endoskopia kapsułkowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 4
• Strony: 782--793
• Opis fizyczny: Bibliogr. 41 poz., rys., tab., wykr.

Twórcy

autor

Jagadeesan A.

• Department of Electronics & Instrumentation Engineering, Bannari Amman Institute of Technology, Erode, India

autor

Sivaraman J.

• Department of Biotechnology & Medical Engineering, National Institute of Technology, Rourkela, India

Bibliografia

• [1] Sailer J, Zacherl J, Schima W. MDCT of small bowel tumours. Cancer Imaging 2007;7:224–33.
• [2] Masselli G. Small bowel imaging: clinical applications of the different imaging modalities – a comprehensive review. ISRN Pathol 2013;1–13.
• [3] Levine MS, Rubesin SE, Laufer I. Barium studies in modern radiology: do they have a role? Radiology 2009;250:18–22.
• [4] Ilangovan R, Burling D, George A, Gupta A, Marshall M, Taylor SA. CT enterography: review of technique and practical tips. Br J Radiol 2012;85:876–86.
• [5] Hara AK, Leighton JA, Sharma VK, Heigh RI, Fleischer DE. Imaging of small bowel disease: comparison of capsule endoscopy, standard endoscopy, barium examination, and CT. Radiographics 2005;25:697–711.
• [6] Johanssen S, Boivin M, Lochs H, Voderholzer W. The yield of wireless capsule endoscopy in the detection of neuroendocrine tumors in comparison with CT enteroclysis. Gastrointest Endosc 2006;63:660–5.
• [7] Moy MP, Sauk J, Gee MS. The role of MR enterography in assessing Crohn's disease activity and treatment response. Gastroent Res Pract 2016;1–13.
• [8] Lai C, Zhou HC, Ma M, Zhang HX, Jia X. Comparison of magnetic resonance enterography, capsule endoscopy and gastrointestinal radiography of children with small bowel Crohn's disease. Exp Ther Med 2013;6:115–20.
• [9] Hara AK, Leighton JA, Sharma VK, Fleischer DE. Small bowel: preliminary comparison of capsule endoscopy with barium study and CT. Radiology 2004;230:260–5.
• [10] Costamagna G, Shah SK, Riccioni ME, Foschia F, Mutignani M, Perri V, et al. A prospective trial comparing small bowel radiographs and video capsule endoscopy for suspected small bowel disease. Gastroenterology 2002;123:999–1005.
• [11] Maieron A, Hubner D, Blaha B, Deutsch C, Schickmair T, Ziachehabi A, et al. Multicenter retrospective evaluation of capsule endoscopy in clinical routine. Endoscopy 2004;36:864–8.
• [12] Alder DG, Gostout CJ. Wireless capsule endoscopy. Hosp Physician 2003;5:14–22.
• [13] Liu G, Yan G, Kuangand 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.
• [14] 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;16:323–9.
• [15] Li B, Meng MQH, Lau JYW. Computer-aided small bowel tumor detection for capsule endoscopy. Artif Intell Med 2011;52:11–6.
• [16] Li B, Meng MQH. Automatic polyp detection for wireless capsule endoscopy images. Expert Syst Appl 2012;39:10952–8.
• [17] Barbosa DC, Roupar DB, Ramos JC, Tavares AC, Lima CS. Automatic small bowel tumor diagnosis by using multiscale wavelet-based analysis in wireless capsule endoscopy images. Biomed Eng Online 2012;11:1–17.
• [18] Karargyris A, Bourbakis N. Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos. IEEE Trans Biomed Eng 2011;58:2777–86.
• [19] Sokhanvar S, Packirisamy M, Dargahi J. A multifunctional PVDF-based tactile sensor for minimally invasive surgery. Smart Mater Struct 2007;16:989–98.
• [20] Ayyildiz M, Guclu B, Yildiz M, Basdogan C. A novel tactile sensor for detecting lumps in breast tissue, haptics: generating and perceiving tangible. Sensations 2010;367–72.
• [21] Ayyildiz M, Guclu B, Yildiz MZ, Basdogan C. An optoelectromechanical tactile sensor for detection of breast lumps. IEEE Trans Haptics 2013;6:145–55.
• [22] Nguyen CV, Saraf RF. Tactile imaging of an imbedded palpable structure for breast cancer screening. ACS Appl Mater Interfaces 2014;6:16368–74.
• [23] Tanaka Y, Fukuda T, Fujiwara M, Sano A. A tactile sensor using acoustic reflection for lump detection in laparoscopic surgery. Int J Comput Assist Radiol Surg 2015;10:183–93.
• [24] Astrand AP, Andersson BM, Jalkanen V, Lindahl OA. Initial measurements on whole human prostate ex vivo with a tactile resonance sensor in order to detect prostate cancer. IFMBE Proceedings 2015;48:120–3.
• [25] Chuang C, Li TH, Chou IC, Teng Y. Piezoelectric tactile sensor for submucosal tumor hardness detection in endoscopy. Solid-State Sens Actuators Microsyst 2015;871–5.
• [26] Li B, Shi Y, Fontecchio A, Visell Y. Mechanical imaging of soft tissues with a highly compliant tactile sensing array. IEEE Trans Biomed Eng 2018;65:687–97.
• [27] Zhang L, Yu F, Cao Y, Wang Y, Chen B. A tactile sensor for measuring hardness of soft tissue with applications to minimally invasive surgery. Sens Actuators A Phys 2017;266:197–204.
• [28] Naidu AS, Naish MD, Patel RV. A breakthrough in tumor localization: combining tactile sensing and ultrasound to improve tumor localization in robotics-assisted minimally invasive surgery. IEEE J Robot Autom 2017;1070:54–62.
• [29] Astrand AP, Andersson BM, Jalkanen V, Ljungberg B, Berg A, Lindahl OA. Prostate cancer detection with a tactile resonance sensor—measurement considerations and clinical setup. Sensors 2017;17. E2453.
• [30] Wang L. Microwave sensors for breast cancer detection. Sensors 2018;18. E365.
• [31] Beccani M, Natalai CD, Hall NE, Benjamin CE, Bell CS, Valdastri P. Wireless tissue palpation: characterization of the probe head to improve detection of tumors in soft tissue. Proc Eng 2014;87:352–5.
• [32] Gubenko MM, Morozov AV, Lyubicheva AN, Goryacheva IG, Dosaev MZ, Ju M-S, et al. Video-tactile pneumatic sensor for soft tissue elastic modulus estimation. Biomed Eng Online 2017;16:94–105.
• [33] Ojala T, Pietikainen M, Harwood D. A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 1996;29:51–9.
• [34] Ojala T, Pietikainen M, Maenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 2002;24:971–87.
• [35] Nawarathna R, Oh JH, Muthukudage J, Tavanapong W, Wong J, de Groen PC, et al. Abnormal image detection in endoscopy videos using a filter bank and local binary patterns. Neurocomputing 2014;144:70–91.
• [36] Mackiewicz M, Berens J, Fisher M. Wireless capsule endoscopy colour video segmentation. IEEE Trans Med Imaging 2008;27:1769–81.
• [37] Maghsoudi OH, Alizadeh M, Mirmomen M. A computer aided method to detect bleeding, tumor, and disease regions in wireless capsule endoscopy. Proc IEEE Signal Processing in Medicine and Biology Symposium (SPMB) 2016;1.
• [38] Yixuan Y, Meng MQH. Deep learning for polyp recognition in wireless capsule endoscopy images. Med Phys 2017;44 (4):1379–89.
• [39] Alizadeh M, Maghsoudi OH, Sharzehi K, Hemati HR, Kamal A, Talebpour A. Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system. J Biomed Res 2017;31(5):419–27.
• [40] 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.
• [41] Maghsoudi OH. Super-pixel based segmentation and classification of polyps in wireless capsule endoscopy; 2018, https://arxiv.org/abs/1710.07390.