Abstract
In a large experimental study, the impact of lossy image compression standards on CAD support systems based on texure classification is assessed using colonoscopic imagery as an example. Results clearly indicate that (1) it is important to compress both training and evaluation data involved in the classification process, (2) there is a big difference if initial data is precompressed or uncompressed, and (3) in the latter case significant improvements in terms of classification accuracy may be achieved, even and especially in case of high compression ratios. Moreover it is found that compression efficiency in terms of image quality metrics and/or human perception is not correlated with the impact compression has on texture classification accuracy.
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
Similar content being viewed by others
References
Belloulata, K., Baskurt, A., Benoit-Cattin, H., Prost, R.: Fractal coding of medical images. In: Kim, Y. (ed.) Medical Imaging 1996: Image Display. SPIE Proceedings, vol. 2707, pp. 598–609. SPIE, Newport Beach (1996)
Chen, M., Zhang, S., Karim, M.: Modification of standard image compression methods for correlation-based pattern recognition. Opt. Eng. 43(8), 1723–1730 (2004)
Cosman, P.C., et al.: Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy. Proc. IEEE 82(6), 919–932 (1994)
Delac, K., et al.: Image compression in face recognition - a literature survey. In: Delac, K., et al. (ed.) Recent Advances in Face Recognition, pp. 236–250. I-Tech (2008)
Garcia-Vichez, F., Munoz-Mari, J., Zortea, M., Blanes, I., Gonzales-Ruiz, V., Camps-Valls, G.: On the impact of lossy compression on hyperspectral image classification and unmixing. IEEE Geosci. Remote Sens. Lett. 8(2), 253–257 (2011). doi:10.1109/LGRS.2010.2062484
Häfner, M., et al.: Computer-aided classification of zoom-endoscopical images using fourier filters. IEEE Trans. Inf. Technol. Biomed. 14(4), 958–970 (2010)
Hämmerle-Uhl, J., Karnutsch, M., Uhl, A.: Evolutionary optimisation of JPEG2000 part 2 wavelet packet structures for polar iris image compression. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013, Part I. LNCS, vol. 8258, pp. 391–398. Springer, Heidelberg (2013)
Jeong, G.M., Kim, C., Ahn, H.S., Ahn, B.J.: JPEG quantization table design for face images and its application to face recognition. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E69–A(11), 2990–2993 (2006)
Kato, S., et al.: Magnifying colonoscopy as a non-biopsy technique for differential diagnosis of non-neoplastic and neoplastic lesions. World J. Gastroenterol. 12(9), 1416–1420 (2006)
Konrad, M., Stögner, H., Uhl, A.: Custom design of JPEG quantisation tables for compressing iris polar images to improve recognition accuracy. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1091–1101. Springer, Heidelberg (2009)
Konsti, J., et al.: Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium. Diagn. Pathol. 7(29) (2012). doi:10.1186/1746-1596-7-29
Lau, W.L., Li, Z.L., Lam, W.K.: Effects of JPEG compression on image classification. Int. J. Remote Sens. 24(7), 1535–1544 (2003)
Panych, L.: Theoretical comparison of Fourier and wavelet encoding in magnetic resonance imaging. IEEE Trans. Med. imaging 15(2), 141–153 (1997)
Rabenstein, T., et al.: Tele-endoscopy: influence of data compression, bandwidth and simulated impairments on the usability of real-time digital video endoscopy transmissions for medical diagnoses. Endoscopy 34(9), 703–710 (2002)
Rathgeb, C., et al.: Effects of severe image compression on iris segmentation performance. In: Proceedings of the IAPR/IEEE International Joint Conference on Biometrics (IJCB 2014) (2014)
Santalla, H., et al.: Effects on MR images compression in tissue classification quality. J. Phys. Conf. Ser. 90(1) (2007)
Schoeffmann, K., et al.: Investigation of the impact of compression on the perceptional quality of laparoscopic videos. In: Proceedings of the 27th International Symposium on Computer-Based Medical Systems (CBMS 2014), pp. 153–158 (2014)
Tamaki, T., et al.: Computer-aided colorectal tumor classification in nbi endoscopy using local features. Med. Image Anal. 17(1), 78–100 (2013)
Wong, S., et al.: Radiologic image compression - a review. Proc. IEEE 83(2), 194–219 (1995)
Zabala, A., Pons, X.: Effects of lossy compression on remote sensing image classification of forest areas. Int. J. Appl. Earth Obs. Geoinf. 13(1), 43–51 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Elmer, P. et al. (2016). Impact of Lossy Image Compression on CAD Support Systems for Colonoscopy. In: Luo, X., Reichl, T., Reiter, A., Mariottini, GL. (eds) Computer-Assisted and Robotic Endoscopy. CARE 2015. Lecture Notes in Computer Science(), vol 9515. Springer, Cham. https://doi.org/10.1007/978-3-319-29965-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-29965-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29964-8
Online ISBN: 978-3-319-29965-5
eBook Packages: Computer ScienceComputer Science (R0)