نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی برق ، دانشکده مهندسی برق، دانشگاه صنعتی شریف، تهران

2 استادیار ، دانشکده مهندسی برق، دانشگاه صنعتی شریف، تهران

10.22041/ijbme.2017.64958.1222

چکیده

در این مقاله، الگوریتمی جدید بر‌مبنای حسگری فشرده برای بازسازی تصویر در سیستم تصویربرداری مقطع­نگاری کامپیوتری  (CT) ارائه شده است.  هدف اصلی، کاهش زمان اسکن در تصویربرداری CT و بنابراین دوز اشعة تابشی، بدون کاهش کیفیت تصویر است. برای بهبود کیفیت تصویر بازسازی شده توسط تعداد نمونه­های کم دریافتی، تابع هزینة جدیدی شامل ترکیب بهینه‌ای از ضرایب تبدیل موجک و ضرایب تبدیل کسینوسی و واریانس مجموع، ارائه شده است. کیفیت تصاویر به‌دست‌آمده با تصاویر حاصل از تکنیک‌های پیشین حسگری فشرده، بر‌اساس معیارهای متوسط مربعات خطا (MSE)، بیشینه نسبت سیگنال به نویز (PSNR) و تشابه ساختار (SSIM)، به‌صورت کمی مقایسه شده است. نتایج، نشان‌دهندة آن است که روش پیشنهادی قادر به تولید تصاویر با کیفیت بالاتر و حفظ بهتر لبه، در عین کاهش مصنوعات تصویر با استفاده از تعداد زوایای دید محدود، است. این نتایج، بهبود قابل ملاحظه­ای نسبت به نتایج الگوریتم‌های فشرده‌سازی پیشین از دیدگاه کیفیت تصویر بازسازی‌شده دارند.
 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Improved Image Quality in Reduced Radiation Computerized Tomography Using Compressed-Sensing and DWT and DCT Coefficients

نویسندگان [English]

  • Hassan Abbasi 1
  • zahra kavehvash 2

1 Ph.D Student, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

2 Assistant Professor, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

چکیده [English]

A novel computerized tomographic (CT) imaging structure based on the theory of compressed sensing (CS) is proposed. The main goal is to mitigate the CT imaging time and thus x-ray radiation dosage without compromising the image quality. In this study, we propose to use a novel dictionary in compressed sensing algorithm. Our dictionary is an optimal combination of Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV) transform. We utilize three quality assessment metrics including mean square error (MSE), peak signal to noise ratio (PSNR) and structural similarity (SSIM) indices to quantitatively evaluate the reconstructed images. The results show that the proposed method can generate high quality images with less artifacts while preserving edges when fewer number of view angles are used for reconstruction in a CT imaging system. This is in comparison with those results obtained from other reconstruction algorithms in view of the reconstructed image quality. 

کلیدواژه‌ها [English]

  • Computerized Tomography
  • Compressed Sensing
  • Total Variation
  • Wavelet transform
  • Cosine Transform

   [1]      J. Radon. “Uber due bestimmung von funktionendurchihreintergralwertelangsgewissermannigfaltigkeiten (on the determination of functions from their integrals along certain manifolds,” BerichteSaechsischeAkademie der Wissenschaften, vol. 29, pp. 262 - 277, 1917.

   [2]      G. IIounsfield, “A method of an apparatus for examination of a body by radiation such as x-ray or gamma radiation,” Patent specification 1283915, The Patent Office, 1972.

   [3]      SJ. LaRoque, EY. Sidky, X. Pan, "Accurate imagereconstructionfrom few-view and limited-angle data in diffractiontomography," , Journal of the Optical Society of America, Vol. 25, Issue 7, pp. 1772-1782, Jun 2008.

   [4]      E. Y. Sidky and X. C. Pan, "Image reconstruction in circularcone-beam computed tomography by constrained, totalvariationminimization", Physics in Medicine and Biology, vol. 53, pp. 777 - 807, 2008.

   [5]      J. Bian, J. Wang, X. Han, E. Y. Sidky, L. Shao, and X. Pan,"Optimization-based image reconstruction from sparse-viewdatain offset-detector CBCT", Physics in Medicine and Biology, vol. 58, pp. 205 - 230, 2013.

   [6]      P. T. Lauzier and G. H. Chen, "Characterization of statisticalprior image constrained compressed sensing. I. applicationsto time-resolved contrast-enhanced CT", Medical Physics, vol. 39, pp. 5930 - 5948, 2012.

   [7]      PT. Lauzier, GH. Chen, "Characterization of statistical priorimage constrained compressed sensing (PICCS): II. applicationto dose reduction", Medical Physics, vol. 40, 2013.

   [8]      O. Barkan, J. Weill, A. Averbuch, S. Dekel,"Adaptive CompressedTomography Sensing",IEEEConferance,Computer Visionand Pattern Recognition (CVPR),pp. 2195 - 2202, 2013.

   [9]      W. Hou, et al, "A Compressed Sensing Approach to LowradiationCT Reconstruction", Communication Systems, Networks& Digital Signal Processing(CSNDSP),pp. 793 - 797, 2014.

[10]      H. Abbasi, et al, “Improved CT Image Reconstruction Through Partial Fourier Sampling”, Scientia Iranica D, vol. 23, no. 6, pp.  2908-2916, 2016.

[11]      M Lakshminarayana, Mrinal Sarvagya, "Random sample measurement and reconstruction of medical image signal using Compressive Sensing", Computing and Network Communications (CoCoNet) 2015 International Conference on, pp. 255-262, 2015.

[12]      Qureshi, Muhammad Ali, and M. Deriche. "A New Wavelet Based Efficient Image Compression Algorithm Using Compressive Sensing". Multimedia Tools and Applications 75.12 (2015): 6737-6754. Web. 28 May 2017.

[13]      L. M. Merino and L. E. Mendoza, "Robust compression using Compressive Sensing (CS)," 2010 IEEE ANDESCON, Bogota, 2010, pp. 1-7.

[14]      Kristie D’Ambrosio, et al. “Assessing the Benefits of DCT Compressive Sensing for Computational Electromagnetics”, Master of Engineering in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology April 2011.

[15]      David S. Lalush, Miles N. Wernick, Chapter 21-Iterative Image Reconstruction, Emission Tomography (First Edition), 2004, Pages 443-472.

[16]      R. Gordon, R. Bender, and G. Herman, “Algebraic reconstruction techniques(ART) for three dimensional electron microscopy and X-ray photography,” Journal of Theoretical Biology, vol. 36, pp. 105-117, 1970.

[17]      R. Gordon, “A tutorial on ART (Algebraic Reconstruction Techniques),” Transactions on Nuclear Science, vol. NS-21, pp. 78-93, 1974.

[18]      R.P.V. Rao, R.D. Kriz, A.L. Abbott, C.J. Ribbens, “Parallel implementation of the filtered back projection algorithms for tomographic imaging,” http: //www.sv.vt.edu/xrayct/parallel/ParallelCT. html, ١٩٩۵.

[19]      L. Shepp and B. Logan, “The Fourier reconstruction of a head section,” IEEETransactions on Nuclear Science, vol. NS-21, pp. 21-43, 1974.

[20]      Diederich S, Lenzen H, “Radiation exposure associated with imaging of thechest: comparison of different radiographic and computed tomography techniques,”American Cancer Society, vol. 89, pp. 2457-60, 2000.

[21]      M. Elad, "Optimized projections for compressed sensing," Signal
Processing, IEEE Transactions on, vol. 55, pp. 5695-5702, 2007.

[22]      Zhang, et al. “Relationship between reconstruction quality and scan type for compressive sensing based on cone beam CT reconstruction”, Proc. SPIE, volume 10070, pages 100701M-100701M-10, 2017.

[23]      K. Rao, P. Yip, "Discrete cosine transform: algorithms, advantages, applications," Academic press, 2014.

[24]      S. G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation." IEEE transactions on pattern analysis and machine intelligence, vol. 11, no. 7 pp. 674-693. 1989.

[25]      M. A. Qureshi, M. Deriche. "A new wavelet based efficient image compression algorithm using compressive sensing." Multimedia Tools and Applications, vol. 75, pp.  6737-6754., 2016.

[26]      A. Katunin, M. Dańczak, and P. Kostka. "Automated identification and classification of internal defects in composite structures using computed tomography and 3D wavelet analysis." Archives of Civil and Mechanical Engineering, vol. 15, no. 2, pp. 436-448, 2015.

[27]      V. Bhateja, H. Patel A. Krishn A. Sahu,  A. Lay-Ekuakille "Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transform domains." IEEE Sensors Journal, vol. 15, no. 12, pp. 6783-6790, 2015.

[28]      L. Ritschl, F. Bergner, C. Fleischmann, M. Kachelriess, "Improved total variation-based CT image reconstruction applied to clinical data." Physics in medicine and biology, vol. 56, no. 6, pp. 545, 2011.

[29]      S. O. Jin, J. G. Kim, S. Y. Lee, O. K. Kwon, "Bone-induced streak artifact suppression in sparse-view CT image reconstruction." Biomedical engineering online, vol. 11, no. 1, pp. 11-44, 2012.

[30]      X. Li, S. Luo, "A compressed sensing-based iterative algorithm for CT reconstruction and its possible application to phase contrast imaging." Biomedical engineering online, vol. 10, no. 1, pp. 73, 2011.

[31]      Q. Xu,  H. Yu,  X. Mou,  L. Zhang,  J. Hsieh,  G. Wang, "Low-dose X-ray CT reconstruction via dictionary learning." IEEE Transactions on Medical Imaging, vol. 31, no. 9, pp. 1682-1697, 2012.

[32]      Y. LiuJ. MaH. ZhangJ. WangZ. Liang, "Low-dose CT image reconstruction by adaptive-weighted TV-constrained penalized weighted least-squares approach." Proceedings of The Second International Conference on Image Formation in X-Ray Computed Tomography. 2014.

[33]      M. Belge, M. E. Kilmer, E. L. Miller "Efficient determination of multiple regularization parameters in a generalized L-curve framework," Inverse Problems, vol. 18, no. 4, pp. 1161, 2002.

[34]      Z. Tian Z, X. Jia, K. Yuan, T. Pan, S. Jiang,  "Low-dose CT reconstruction via edge-preserving total variation regularization." Physics in medicine and biology, vol. 56, no. 18, pp.5949, 2011.