Abstract
Image enhancement technologies, such as chromoendoscopy and digital chromoendoscopy were reported to facilitate the detection and diagnosis of colonic polyps during endoscopic sessions. Here, we investigate the impact of enhanced imaging technologies on the classification accuracy of computer-aided diagnosis systems. Specifically, we determine if image representations obtained from different imaging modalities are significantly different and experimentation is performed to figure out the impact of utilizing differing imaging modalities in the training and validation sets. Finally, we examine if merging the images of similar imaging modalities for training the classification model can be effectively applied to improve the accuracy.
G. Wimmer, M. Gadermayr—Equal contributions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Basford, P., Longcroft, G., Bhandari, P.: Pwe-186 iscan in the evaluation of small colonic polyps: outcomes, learning curve from a large prospective series. Gut 61(2), A372 (2012)
Bouwens, M., de Ridder, R., Masclee, A., Driessen, A., Riedl, R., Winkens, B., Sanduleanu, S.: Optical diagnosis of colorectal polyps using high-definition i-scan: an educational experience. World J. Gastroenterol. 19(27), 4334–4343 (2013)
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: British Machine Vision Conference, BMVC 2014, Nottingham, UK, 1–5 September 2014
Easley, G., Labate, D., Lim, W.Q.: Sparse directional image representations using the discrete shearlet transform. Appl. Comput. Harmonic Anal. 25(1), 25–46 (2008)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A.: A kernel two-sample test. JMLR 13, 723–773 (2012)
Gross, S., Palm, S., Tischendorf, J.J.W., Behrens, A., Trautwein, C., Aach, T.: Automated classification of colon polyps in endoscopic image data. In: SPIE Proceedings, vol. 8315, pp. 83150W–83150W-8 (2012)
Häfner, M., Uhl, A., Wimmer, G.: A novel shape feature descriptor for the classification of polyps in HD colonoscopy. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds.) MCV 2013. LNCS, vol. 8331, pp. 205–213. Springer, Heidelberg (2014). doi:10.1007/978-3-319-05530-5_20
Häfner, M., Liedlgruber, M., Uhl, A., Vécsei, A., Wrba, F.: Color treatment in endoscopic image classification using multi-scale local color vector patterns. Med. Image Anal. 16(1), 75–86 (2012)
Häfner, M., Liedlgruber, M., Uhl, A., Vécsei, A., Wrba, F.: Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy. Comput. Methods Programs Biomed. 107(3), 565–581 (2012)
Häfner, M., Uhl, A., Wimmer, G.: Shape and size adapted local fractal dimension for the classification of polyps in HD colonoscopy. In: Proceedings of the IEEE International Conference on Image Processing 2014 (ICIP 2014), pp. 2299–2303, October 2014
Häfner, M., Uhl, A., Wimmer, G.: Shape and size adapted local fractal dimension for the classification of polyps in HD colonoscopy. In: Proceedings of the IEEE International Conference on Image Processing 2014 (ICIP 2014), October 2014
Häfner, M., Kwitt, R., Uhl, A., Gangl, A., Wrba, F., Vecsei, A.: Feature extraction from multi-directional multi-resolution image transformations for the classification of zoom-endoscopy images. Pattern Anal. Appl. 12(4), 407–413 (2009)
Häfner, M., Tamaki, T., Tanaka, S., Uhl, A., Wimmer, G., Yoshida, S.: Local fractal dimension based approaches for colonic polyp classification. Med. Image Anal. 26, 92–107 (2015)
Hegenbart, S., Uhl, A., Vécsei, A.: Survey on computer aided decision support for diagnosis of celiac disease. Comput. Biol. Med. 65, 348–358 (2015)
Hoffman, A., Kagel, C., Goetz, M., Tresch, A., Mudter, J., Biesterfeld, S., Galle, P., Neurath, M., Kiesslich, R.: Recognition and characterization of small colonic neoplasia with high-definition colonoscopy using i-scan is as precise as chromoendoscopy. Dig. Liver Dis. 42(1), 45–50 (2010)
Kato, S., Fu, K.I., Sano, Y., Fujii, T., Saito, Y., Matsuda, T., Koba, I., Yoshida, S., Fujimori, T.: Magnifying colonoscopy as a non-biopsy technique for differential diagnosis of non-neoplastic and neoplastic lesions. World J. Gastroenterol. 12(9), 1416–1420 (2006)
Kiesslich, R.: Advanced imaging in endoscopy. Eur. Gastroenterol. Hepatol. Rev. 5(1), 22–25 (2009)
Kingsbury, N.G.: The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters. In: Proceedings of the IEEE Digital Signal Processing Workshop, DSP 1998, pp. 9–12. Bryce Canyon, USA, August 1998
Kodashima, S., Fujishiro, M.: Novel image-enhanced endoscopy with i-scan technology. World J. Gastroenterol. 16(9), 1043–1049 (2010)
Kovesi, P.D.: Image features from phase congruency. Videre. J. Comput. Vision. Res. 1(3), 2–26 (1999)
Kudo, S.E., Hirota, S., Nakajima, T., Hosobe, S., Kusaka, H., Kobayashi, T., Himori, M., Yagyuu, A.: Colorectal tumours and pit pattern. J. Clin. Pathol. 47, 880–885 (1994)
Kwitt, R., Uhl, A.: Modeling the marginal distributions of complex wavelet coefficient magnitudes for the classification of zoom-endoscopy images. In: Proceedings of the IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2007), Rio de Janeiro, Brasil, pp. 1–8 (2007)
Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)
Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: Cnn features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014, pp. 512–519 (2014)
Tamaki, T., Yoshimuta, J., Kawakami, M., Raytchev, B., Kaneda, K., Yoshida, S., Takemura, Y., Onji, K., Miyaki, R., Tanaka, S.: Computer-aided colorectal tumor classification in NBI endoscopy using local features. Med. Image Anal. 17(1), 78–100 (2013)
Testoni, P., Notaristefano, C., Vailati, C., Leo, M.D., Viale, E.: High-definition colonoscopy with i-scan: better diagnosis for small polyps and flat adenomas. World J. Gastroenterol. 18(37), 5231–5239 (2012)
Varma, M., Garg, R.: Locally invariant fractal features for statistical texture classification. In: Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1–8, October 2007
Wimmer, G., Tamaki, T., Tischendorf, J., Häfner, M., Tanaka, S., Yoshida, S., Uhl, A.: Directional wavelet based features for colonic polyp classification. Med. Image Anal. 31, 16–36 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wimmer, G., Gadermayr, M., Kwitt, R., Häfner, M., Merhof, D., Uhl, A. (2017). Evaluation of i-Scan Virtual Chromoendoscopy and Traditional Chromoendoscopy for the Automated Diagnosis of Colonic Polyps. In: Peters, T., et al. Computer-Assisted and Robotic Endoscopy. CARE 2016. Lecture Notes in Computer Science(), vol 10170. Springer, Cham. https://doi.org/10.1007/978-3-319-54057-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-54057-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54056-6
Online ISBN: 978-3-319-54057-3
eBook Packages: Computer ScienceComputer Science (R0)