Abstract
We verified compression efficiency of the procedures based on compressive sensing (CS) inspiration. Medical imaging was concerned as challenging area of possible applications. Irreversible image compression algorithm was integrated with source data measurements according to CS rules. Two kinds of measurements as inner products against adjusted atoms were used: regular cosines and pseudo-random noiselets. Image coarse representation was approximated from linear cosine measurements while important details were estimated basing on fixed noiselet measurements. Simulated sensor system projected the image onto a set of separable 2-D basis functions to measure the corresponding expansion coefficients. Such procedure was optimized and augmented to construct integrated method of image sensing, compression and data processing. We proposed algorithm of selected measurements with uniformly quantized coefficients formed and encoded with necessary side information. Universal PAQ8 archiver was used to complete compression procedure. Experimentally verified compression schemes showed possible compression improvement by designed procedure in comparison to reference JPEG and JPEG2000 encoders.
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
Preview
Unable to display preview. Download preview PDF.
References
Baraniuk, R.G.: More is less: signal processing and the data deluge. Science 331, 717–719 (2011)
Canadian Association of Radiologists (CAR): CAR standards for irreversible compression in digital diagnostic imaging within radiology
Candés, E., Romberg, J.: Sparsity and incoherence in compressive sampling. Inverse Problems 23(3), 969–985 (2007)
Candés, E., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52(2), 489–509 (2006)
Candés, E., Romberg, J., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure and Applied Mathematics 59(8), 1207–1223 (2006)
Coifman, R., Geshwind, F., Meyer, Y.: Noiselets. Applied and Computational Harmonic Analysis 10(1), 27–44 (2001)
Finnis, K.: A review and comparison of medical image compression. Technical report for Canada Health Infoway (2004)
Fornasier, M., Rauhu, H.: Iterative thresholding algorithms. Applied and Computational Harmonic Analysis 25(2), 187–208 (2008)
Goyal, V.K., Fletcher, A.K., Rangan, S.: Compressive sampling and lossy compression. IEEE Signal Processing Magazine 25(2), 48–56 (2008)
Koff, D.A., Shulman, H.: An overview of digital compression of medical images: can we use lossy image compression in radiology? Canadian Association of Radiologists Journal 57(4), 211–217 (2006)
Mallat, S., Zhang, Z.: Matching Pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing 41(12), 3397–3415 (1993)
Przelaskowski, A.: Irreversible medical image compression: conditions of acceptability. Task Quarterly 8(2), 303–316 (2004)
Przelaskowski, A., Jozwiak, R.: Compressive measurements with integrated sensing, compression and data processing – initial study. In: Proceedings IEEE PCS 2012 (to appear, 2012)
Romberg, J.: Imaging via compressive sampling [introduction to compressive sampling and recovery via convex programming]. IEEE Signal Processing Magazine 25(2), 14–20 (2008)
Sadoughi, B., Brown, S.M., Nachlas, N.E., Fried, M.P.: Image-guided surgery. Medscape Reference, http://emedicine.medscape.com/article/875524-overview
Schulz, A., Velho, L., Silva, E.A.B.D.: On the empirical rate-distortion performance of Compressive Sensing. In: Proc. IEEE ICIP 2009, pp. 3049–3052 (2009)
Seeram, E.: Irreversible compression in digital radiology. A literature review. Radiography 12(1), 45–59 (2006)
Tadeusiewicz, R.: What Does it Means ”Automatic Understanding of the Images”? In: Proceedings IEEE IST 2007, pp. 1–3 (2007)
Tadeusiewicz, R., Szczepaniak, P.S.: Basic Concepts of Knowledge-Based Image Understanding. In: Nguyen, N.T., Jo, G.-S., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2008. LNCS (LNAI), vol. 4953, pp. 42–52. Springer, Heidelberg (2008)
Wen, J., Chen, Z., Han, Y., Villasenor, J., Yang, S.: A compressive sensing image compression algorithm using quantized DCT and noiselet information. In: Proceedings IEEE ICASSP 2010, pp. 1294–1297 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Przelaskowski, A., Jozwiak, R. (2012). Sensed Compression with Cosine and Noiselet Measurements for Medical Imaging. In: Piętka, E., Kawa, J. (eds) Information Technologies in Biomedicine. Lecture Notes in Computer Science(), vol 7339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31196-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-31196-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31195-6
Online ISBN: 978-3-642-31196-3
eBook Packages: Computer ScienceComputer Science (R0)