Medical Imaging and Its Objective Quality Assessment: An Introduction

With the rise in research on applications of medical image processing, the evaluation of parameters and techniques required for measurement of medical image quality is the need of the hour. The effective, yet automatic methods for measurement of quality of a medical image are of particular interest. This chapter is an overview of different medical imaging technologies, and the related image quality assessment (IQA) algorithms. The main focus is on objective assessment (OA), rather than subjective assessment (SA). Three types of OA-based IQA algorithms are presented in detail: full reference-based IQA (FR-IQA) algorithms; no reference-based IQA (NR-IQA) algorithms and reduced reference-based IQA (RR-IQA) algorithms.

[1]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[2]  Wen Gao,et al.  Reduced reference image quality assessment using entropy of primitives , 2013, 2013 Picture Coding Symposium (PCS).

[3]  Rajiv Kapoor,et al.  Statistically matched wavelet-based method for detection of power quality events , 2011 .

[4]  Teresa Chambel,et al.  Get Around 360º Hypervideo Its Design and Evaluation , 2012, Int. J. Ambient Comput. Intell..

[5]  D. Burr,et al.  Mach bands are phase dependent , 1986, Nature.

[6]  Valero Laparra,et al.  Divisive normalization image quality metric revisited. , 2010, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[8]  Joni-Kristian Kämäräinen,et al.  Simple Gabor feature space for invariant object recognition , 2004, Pattern Recognit. Lett..

[9]  Wen Gao,et al.  Entropy of primitive: A top-down methodology for evaluating the perceptual visual information , 2013, 2013 Visual Communications and Image Processing (VCIP).

[10]  D. Burr,et al.  Feature detection in human vision: a phase-dependent energy model , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[11]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[12]  Jesús Malo,et al.  Characterization of the human visual system threshold performance by a weighting function in the Gabor domain , 1997 .

[13]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[14]  Ashish Kapoor,et al.  Learning a blind measure of perceptual image quality , 2011, CVPR 2011.

[15]  Nilanjan Dey,et al.  Parameter Optimization for Local Polynomial Approximation based Intersection Confidence Interval Filter Using Genetic Algorithm: An Application for Brain MRI Image De-Noising , 2015, J. Imaging.

[16]  Debarati Kundu,et al.  Subjective and objective quality evaluation of synthetic and high dynamic range images , 2016 .

[17]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[18]  Alan C. Bovik,et al.  Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality , 2011, IEEE Transactions on Image Processing.

[19]  Vikrant Bhateja,et al.  Image similarity metric (ISIM): a reduced reference image quality assessment approach , 2015, CSI Transactions on ICT.

[20]  Robyn A. Owens,et al.  Feature detection from local energy , 1987, Pattern Recognit. Lett..

[21]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[22]  J A Solomon,et al.  Model of visual contrast gain control and pattern masking. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  Zhou Wang,et al.  Multi-scale structural similarity for image quality assessment , 2003 .

[24]  G. N. Swamy,et al.  Sensitive Digital Image Watermarking for Copyright Protection , 2013, Int. J. Netw. Secur..

[25]  Surekha Borra,et al.  Attendance management system using hybrid face recognition techniques , 2016, 2016 Conference on Advances in Signal Processing (CASP).

[26]  Jitendra Virmani,et al.  A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images: A Decision Support System for Medical Renal Diseases , 2017, Int. J. Ambient Comput. Intell..

[27]  David S. Doermann,et al.  No-reference image quality assessment based on visual codebook , 2011, 2011 18th IEEE International Conference on Image Processing.

[28]  S. Lippman,et al.  The Scripps Institution of Oceanography , 1959, Nature.

[29]  Patrick C. Teo,et al.  Perceptual image distortion , 1994, Proceedings of 1st International Conference on Image Processing.

[30]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.

[31]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[32]  Hongwei Li,et al.  Reduced-reference image quality assessment using moment method , 2016 .

[33]  Đorđe Stojanović,et al.  KONCEPT STRATEŠKE KULTURE: SLUČAJ EU , 2010 .

[34]  Nilanjan Dey,et al.  The Brain Tumor Segmentation Using Fuzzy C-Means Technique: A Study , 2017 .

[35]  Markus Barth,et al.  Contrast‐to‐noise ratio (CNR) as a quality parameter in fMRI , 2007, Journal of magnetic resonance imaging : JMRI.

[36]  Varsha Hemant Patil,et al.  A Study of Vision based Human Motion Recognition and Analysis , 2016, Int. J. Ambient Comput. Intell..

[37]  D. Heeger.  Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[38]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Yair Moshe,et al.  Reduced-reference image quality assessment based on DCT Subband Similarity , 2016, 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX).

[40]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[41]  J. Robson,et al.  Application of fourier analysis to the visibility of gratings , 1968, The Journal of physiology.

[42]  Alberto Leon-Garcia,et al.  Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video , 1995, IEEE Trans. Circuits Syst. Video Technol..

[43]  Peter Kovesi,et al.  Image Features from Phase Congruency , 1995 .

[44]  Yair Moshe,et al.  Image quality assessment based on DCT subband similarity , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[45]  Christophe Charrier,et al.  Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain , 2012, IEEE Transactions on Image Processing.

[46]  Yong Xu,et al.  Reduced reference image quality assessment using regularity of phase congruency , 2014, Signal Process. Image Commun..

[47]  Nilanjan Dey,et al.  Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study , 2017, Neural Computing and Applications.

[48]  S. Margret Anouncia,et al.  A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection , 2016, Int. J. Ambient Comput. Intell..

[49]  Nilanjan Dey,et al.  Automated stratification of liver disease in ultrasound: An online accurate feature classification paradigm , 2016, Comput. Methods Programs Biomed..

[50]  Nilanjan Dey,et al.  Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach , 2015 .

[51]  N. Dey,et al.  DWT-DCT-SVD based intravascular ultrasound video watermarking , 2012, 2012 World Congress on Information and Communication Technologies.