Prevalent degradations and processing challenges of computed tomography medical images: a compendious analysis

Computed Tomography (CT) has remained an important component of medical imaging since its inception. In general, it is preferred to keep the radiation dose as low as possible during the CT examinations to prevent patients as well as operators from the dangerous side effects of these radiations, which in an extreme case may lead to cancer. However, reducing the radiation dose leads to undesirable degradations which not only reduce the visual quality of CT images, but also make such images difficult to interpret in clinical routines. The most common degradations in low-dose CT images include blur, noise and low-contrast. Over the recent years, considerable research has been made to process these degradations. However, they still remain open for research due to the wide variety of challenges they offer. In this article, the causing factors of such degradations are addressed adequately. Furthermore, the challenges that face the processing of these degradations are mentioned in detail. Finally, this article is intended for researchers who are approaching this topic to understand the aforesaid issues extensively.

[1]  Turgay Çelik,et al.  Two-dimensional histogram equalization and contrast enhancement , 2012, Pattern Recognit..

[2]  Soo Yeol Lee,et al.  Adaptive Projection Selection for Computed Tomography , 2013, IEEE Transactions on Image Processing.

[3]  A. Carasso.  Linear and Nonlinear Image Deblurring: A Documented Study , 1999 .

[4]  Theodore L. Economopoulos,et al.  Contrast enhancement of images using Partitioned Iterated Function Systems , 2010, Image Vis. Comput..

[5]  Ming Jiang,et al.  Blind deblurring of spiral CT images , 2003, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

[6]  S. Riad,et al.  Filtering capabilities and convergence of the Van-Cittert deconvolution technique , 1991 .

[7]  Dennis C. Ghiglia.  Space-invariant deblurring given N independently blurred images of a common object , 1984 .

[8]  R. Cury,et al.  Practical tips and tricks in cardiovascular computed tomography: diagnosis of myocardial infarction. , 2009, Journal of cardiovascular computed tomography.

[9]  Desire Sidibé,et al.  A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images , 2012, Comput. Methods Programs Biomed..

[10]  Jongjoo Park,et al.  Contrast enhancement with slope modification , 2014, The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014).

[11]  M. L. Dewal,et al.  Efficient Denoising Technique for CT images to Enhance Brain Hemorrhage Segmentation , 2012, Journal of Digital Imaging.

[12]  B. Bendriem,et al.  Performance Characteristics of a New LSO PET/CT Scanner With Extended Axial Field-of-View and PSF Reconstruction , 2009, IEEE Transactions on Nuclear Science.

[13]  M. Iqbal Saripan,et al.  Computed tomography soft tissue restoration using wiener filter , 2010, 2010 IEEE Student Conference on Research and Development (SCOReD).

[14]  Y. Yamashita,et al.  Low-dose abdominal CT: comparison of low tube voltage with moderate-level iterative reconstruction and standard tube voltage, low tube current with high-level iterative reconstruction. , 2013, Clinical radiology.

[15]  V. Magudeeswaran,et al.  An Efficient Method for Contrast Enhancement in Still Images using Histogram Modification Framework , 2012 .

[16]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Filippo Attivissimo,et al.  A Technique to Improve the Image Quality in Computer Tomography , 2010, IEEE Transactions on Instrumentation and Measurement.

[18]  Rolf Clackdoyle,et al.  Tomographic Reconstruction in the 21st Century , 2010, IEEE Signal Processing Magazine.

[19]  Jianhua Ma,et al.  Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach , 2015, Comput. Medical Imaging Graph..

[20]  Wei Zhi-hui,et al.  Fast gradient-based algorithm for total variation regularized tomography reconstruction , 2011, 2011 4th International Congress on Image and Signal Processing.

[21]  Ching-Chung Yang.  Image enhancement by modified contrast-stretching manipulation , 2006 .

[22]  M. L. Dewal,et al.  Medical image denoising using adaptive fusion of curvelet transform and total variation , 2013, Comput. Electr. Eng..

[23]  Wiro J. Niessen,et al.  Selective Deblurring for Improved Calcification Visualization and Quantification in Carotid CT Angiography: Validation Using Micro-CT , 2009, IEEE Transactions on Medical Imaging.

[24]  Qiang Li,et al.  Recent progress in computer-aided diagnosis of lung nodules on thin-section CT , 2007, Comput. Medical Imaging Graph..

[25]  Ronald M. Summers,et al.  Mesenteric Vasculature-Guided Small Bowel Segmentation on 3-D CT , 2013, IEEE Transactions on Medical Imaging.

[26]  Yo-Sung Ho,et al.  High-quality non-blind image deconvolution with adaptive regularization , 2011, J. Vis. Commun. Image Represent..

[27]  Prabir Kumar Biswas,et al.  Enhancement of dark and low-contrast images using dynamic stochastic resonance , 2013, IET Image Process..

[28]  N. Kakimoto,et al.  CT and MR images of pleomorphic adenoma in major and minor salivary glands. , 2009, European journal of radiology.

[29]  Kok-Swee Sim,et al.  Contrast enhancement of computed tomography images by adaptive histogram equalization‐application for improved ischemic stroke detection , 2012, Int. J. Imaging Syst. Technol..

[30]  Sungyoung Lee,et al.  Homogeneity- and density distance-driven active contours for medical image segmentation , 2011, Comput. Biol. Medicine.

[31]  Xiubao Sui,et al.  Range Limited Bi-Histogram Equalization for image contrast enhancement , 2013 .

[32]  Soosan Beheshti,et al.  Adaptive updating of regularization parameters , 2015, Signal Process..

[33]  David R. Bull,et al.  Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients , 2010, 2010 IEEE International Conference on Image Processing.

[34]  Jacques A. de Guise,et al.  A method for modeling noise in medical images , 2004, IEEE Transactions on Medical Imaging.

[35]  Malcolm Atkinson,et al.  Computed tomography perfusion imaging denoising using Gaussian process regression , 2012, Physics in medicine and biology.

[36]  T. Shimamura,et al.  Image Restoration Based on Edgemap and Wiener Filter for Preserving Fine Details and Edges , 2011 .

[37]  Lei Xing,et al.  Investigation of X-ray Fluorescence Computed Tomography (XFCT) and K-Edge Imaging , 2012, IEEE Transactions on Medical Imaging.

[38]  Ling Xu,et al.  Noise Removal of MRI Data with Edge Enhancing , 2011, 2011 5th International Conference on Bioinformatics and Biomedical Engineering.

[39]  D S Biggs,et al.  Acceleration of iterative image restoration algorithms. , 1997, Applied optics.

[40]  Qianjin Feng,et al.  Improving low-dose abdominal CT images by Weighted Intensity Averaging over Large-scale Neighborhoods. , 2011, European journal of radiology.

[41]  Panos Liatsis,et al.  Medical Image Deblurring via Lagrangian Pursuit in Frame Dictionaries , 2011, 2011 Developments in E-systems Engineering.

[42]  Marco Donatelli,et al.  A fast alternating minimization algorithm for total variation deblurring without boundary artifacts , 2013, 1308.6754.

[43]  Bram van Ginneken,et al.  Local noise weighted filtering for emphysema scoring of low-dose CT images , 2006, IEEE Transactions on Medical Imaging.

[44]  Stefan Wesarg,et al.  Application of Radial Ray Based Segmentation to Cervical Lymph Nodes in CT Images , 2013, IEEE Transactions on Medical Imaging.

[45]  G. S. Panayiotakis,et al.  A review of patient dose and optimisation methods in adult and paediatric CT scanning. , 2012, European journal of radiology.

[46]  Huy,et al.  An Optimal Weight Method for CT Image Denoising , 2012 .

[47]  Jason Cong,et al.  A Hybrid Architecture for Compressive Sensing 3-D CT Reconstruction , 2012, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[48]  Jeffrey A. Fessler,et al.  A Splitting-Based Iterative Algorithm for Accelerated Statistical X-Ray CT Reconstruction , 2012, IEEE Transactions on Medical Imaging.

[49]  Tuan D. Pham,et al.  Supervised restoration of degraded medical images using multiple-point geostatistics , 2012, Comput. Methods Programs Biomed..

[50]  Bin Yan,et al.  Fast Low-Dose Reconstruction From Truncated Data in Dental CT , 2013, IEEE Transactions on Nuclear Science.

[51]  W. Clem Karl,et al.  A novel hybrid algorithm for accelerating CT reconstructions and improving low-dose image quality , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[52]  Temesguen Messay,et al.  Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset , 2015, Medical Image Anal..

[53]  H. Mohamadian,et al.  Comparative and Quantitative Study of Fundamental Approaches on Digital Aerial Image Deblurring , 2012 .

[54]  Qi Li,et al.  A modified Richardson–Lucy algorithm for single image with adaptive reference maps , 2014 .

[55]  Aleksandra Pizurica,et al.  Iterative CT Reconstruction Using Shearlet-Based Regularization , 2013, IEEE Transactions on Nuclear Science.

[56]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[57]  Alfred M. Bruckstein,et al.  On Gabor's contribution to image enhancement , 1994, Pattern Recognit..

[58]  Norio Akamatsu,et al.  A New Approach for Contrast Enhancement Using Sigmoid Function , 2004, Int. Arab J. Inf. Technol..

[59]  Seyed Ghorshi,et al.  Tomographical Medical Image Reconstruction Using Kalman Filter Technique , 2011, 2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications Workshops.

[60]  Khalid M. Amin,et al.  Automatic liver tumor segmentation from CT scans with knowledge-based constraints , 2010, 2010 5th Cairo International Biomedical Engineering Conference.

[61]  Jeffrey A. Fessler,et al.  Forward-Projection Architecture for Fast Iterative Image Reconstruction in X-Ray CT , 2012, IEEE Transactions on Signal Processing.

[62]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.

[63]  Shyam Lal,et al.  Efficient algorithm for contrast enhancement of natural images , 2014, Int. Arab J. Inf. Technol..

[64]  Limin Luo,et al.  De-noising of low-dose CT images using space-time nonlocal means over large-scale neighborhoods , 2011, The 2011 IEEE/ICME International Conference on Complex Medical Engineering.

[65]  V. Sadasivam,et al.  Neural network based texture analysis of CT images for fatty and cirrhosis liver classification , 2015, Appl. Soft Comput..

[66]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[67]  Stanley Osher,et al.  Image Recovery via Nonlocal Operators , 2010, J. Sci. Comput..

[68]  Liyi Zhang,et al.  Research of Medical Image Variable Step-Size Blind Restoration Algorithm , 2012 .

[69]  Lei Liang,et al.  Adaptive Landweber method to deblur images , 2003, IEEE Signal Process. Lett..

[70]  Paul Scheunders,et al.  Wavelet-Based EM Algorithm for Multispectral-Image Restoration , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[71]  Madhu S. Nair,et al.  An Alpha Rooting Based Hybrid Technique for Image Enhancement , 2011 .

[72]  J. Biemond,et al.  Basic Methods for Image Restoration and Identification , 2009 .

[73]  Sherin M. Youssef,et al.  Contourlet-based Feature Extraction for Computer Aided Diagnosis of Medical Patterns , 2011, 2011 IEEE 11th International Conference on Computer and Information Technology.