COMPARISON OF ULTRASOUND IMAGE FILTERING METHODS BY MEANS OF MULTIVARIABLE KURTOSIS
DOI:
https://doi.org/10.5566/ias.1639Keywords:
forward-backward diffusion, multivariate kurtosis, nonlinear coherent diffusion, speckle filtering, ultrasound imagesAbstract
Comparison of the quality of despeckled US medical images is complicated because there is no image of a human body that would be free of speckles and could serve as a reference. A number of various image metrics are currently used for comparison of filtering methods; however, they do not satisfactorily represent the visual quality of images and medical expert’s satisfaction with images. This paper proposes an innovative use of relative multivariate kurtosis for the evaluation of the most important edges in an image. Multivariate kurtosis allows one to introduce an order among the filtered images and can be used as one of the metrics for image quality evaluation. At present there is no method which would jointly consider individual metrics. Furthermore, these metrics are typically defined by comparing the noisy original and filtered images, which is incorrect since the noisy original cannot serve as a golden standard. In contrast to this, the proposed kurtosis is the absolute measure, which is calculated independently of any reference image and it agrees with the medical expert’s satisfaction to a large extent. The paper presents a numerical procedure for calculating kurtosis and describes results of such calculations for a computer-generated noisy image, images of a general purpose phantom and a cyst phantom, as well as real-life images of thyroid and carotid artery obtained with SonixTouch ultrasound machine. 16 different methods of image despeckling are compared via kurtosis. The paper shows that visually more satisfactory despeckling results are associated with higher kurtosis, and to a certain degree kurtosis can be used as a single metric for evaluation of image quality.
References
Buades A, Coll B, Morel JM (2005). A Non-local algorithm for image denoising. In: Proc. IEEE Conf Comput Vis Pattern Recog 2:60-65.
Caviedes J, Oberti F (2004). A new sharpness metric based on local kurtosis, edge and energy information. Signal Process: Image Comm 19:147-61.
Chandler DM (2013). Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Process. Hindawi. Article
ID 905685, 53 pages.
Dansk (2016a). Dansk Fantom Service, http://www.fantom.dk/1525.html Last checked Aug 2016.
Dansk (2016b). Dansk Fantom Service, http://www.fantom.dk/571.htm Last checked Aug 2016.
DeCarlo LT (1997). On the meaning and use of kurtosis. Psychological Methods 2:292-307.
Ferzli R, Karam LJ (2009). A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans Image Process 18:717-28.
Ferzli R, Girjia L, Ali WS (2010). Efficient implementation of kurtosis based no reference image sharpness metric. In: Proc. SPIE, Image Processing: Algorithms and Systems VIII. Vol. 7532, pages 75320E-1 - E-12.
Li L, Lin W, Wang X, Yang G, Bahrami K, Kot AC (2016). No-reference image blur assessment based on discrete orthogonal moments. IEEE
Trans Cybernet 46:39-50.
Lin W, Kuo CC (2011). Perceptual visual quality metrics: a survey. J Vis Comm Image Represent 22:297-312.
Loizou CP, Pattichis CS, Christodoulou CI, Istepanian RS, Pantziaris M, Nicolaides A (2005). Comparative evaluation of despeckle filtering
in ultrasound imaging of the carotid artery. IEEE Trans Ultrason Ferroelectr Freq Control 52:1653-69.
Loizou CP, Pattichis CS, Pantziaris M, Tyllis T, Nicolaides A (2006). Quality evaluation of ultrasound imaging in the carotid artery based on
normalization and speckle reduction filtering. Med Biol Eng Comput 44:414-26.
Loizou CP, Kasparis T, Christodoulides P, Theofanus C, Pantziaris M, Kyriakou E, Pattichis CS (2012). Despeckle filtering in ultrasound video of the common carotid artery. In: Proc IEEE 12th Internat Conf Bioinform Bioeng 721-26.
Loizou CP (2013). Despeckle Filtering Toolbox, matlabsoftware_2013.zip, http://www.medinfo.cs.ucy.ac.cy/index.php/downloads/toolboxes/10-mat
lab-software Last checked Aug 2016.
Loizou CP, Theofanus C, Pantziaris M, Kasparis T (2014). Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery. Comp Methods Programs Biomed 114:109-24.
Loizou CP, Pattichis CS (2015). Despeckle Filtering for Ultrasound Imaging and Video. Vol I: Algorithms and Software, Vol. II: Selected
Applications. 2nd Ed. Morgan & Claypool.
Manjon-Herrera JV (2016). Non-Local Means Filter, http://www.mathworks.com/matlabcentral/fileexchange/13176-non-local-means-filter Last checked Aug 2016.
Mardia KV (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika 57:519-30.
Mateo JL, Fernández-Caballero A (2009). Finding out general tendencies in speckle noise reduction in ultrasound images. Expert Syst with App 36:7786-97.
Mittal A, Soundararajan R, Bovik AC (2013). Making a "completely blind" image quality analyzer. IEEE Signal Process Lett 20:209-12.
Narvekar ND, Karam LJ (2011). A no-reference blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans Image Process
:2678-83.
Nieniewski M (2014). Enhancement of despeckled ultrasound images by forward-backward diffusion. In: Proc Internat Conf Comput Vis Graphics. Lect Not Comput Sci 8671:454-61, Springer.
Nieniewski M, Zajączkowski P (2014). Real-time speckle reduction in ultrasound images by means of nonlinear coherent diffusion using GPU. In:
Proc Internat Conf Comput Vis Graphics. Lect Not Comput Sci 8671:462-69, Springer.
Nieniewski M, Zajączkowski P (2016). Real-time US image enhancement by forward-backward diffusion Using GPU. In: Proc Internat Conf Image
Process Comm Challenges 7. Advances in Intell Syst Computing 389:177-86, Springer.
Pratt WK (2007). Digital Image Processing: Piks Scientific Inside. 4th Ed. Hoboken, NJ: Wiley.
Romeu JL, Ozturk A (1993). A comparative study of goodness-of-fit tests for multivariate normality. J Multivariate Anal 46:309-34.
Rosa R, Monteiro FC (2014). Speckle ultrasound image filtering: performance analysis and comparison. In: Proc Computat Vision Medical
Image Process IV:65-69, Taylor & Francis.
Sanches JM, Laine AF, Suri JS (edits.) (2012). Ultrasound Images. Advances and Applications. New York, NY: Springer.
Schäberle W (2005). Ultrasonography in Vascular Diagnosis. Berlin: Springer.
Sheet D (2016). Pseudo B-mode Ultrasound Image Simulator, http://www.mathworks.com/matlabcentral/fileexchange/34199-pseudo-b-mode-ultrasound-image-simulator Last checked Aug 2016.
Thangavel K, Manavalan R, Aroquiaraj IL (2009). Removal of speckle noise from ultrasound medical image based on special filters: comparative study. ICGST-GVIP J 9(3):25-32.
Thévenaz P (2016). Point Picker, http://bigwww.epfl.ch/thevenaz/pointpicker/ Last checked Aug 2016.
Timm WN (2002). Applied Multivariate Analysis. New York, NY: Springer.
Tong H, Li M, Zhang HJ, Zhang C, He J, Ma WY (2005). Learning no-reference quality metric by examples. In: Proc 11th Internat Conf Multimedia Modellings 247-54.
Virtanen T, Nuutinen M, Vaahteranoksa M, Oittinen P, Häkkinen J (2015). CID2013: a database for evaluating no-reference image quality assessment algorithms. IEEE Trans Image Process 24:390-402.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004). Image quality assessment: from error visibility to structural similarity algorithms. IEEE Trans Image Process 13:600-12.
Wang Z, Bovik AC (2006). Modern Image Quality Assessment. Morgan & Claypool.
Wu Q, Li H, Meng F, Ngan KN, Zhu S (2015). No reference image quality assessment metric via multi-domain structural information and piecewise
regression. J Vis Comm Image Represent 32:205-16.
Zhang NF, Postek MT, Larrabee RD, Vládar AE, Kerry WJ, Jones SN (1999). Image sharpness measurement in the scanning electron microscope
– Part III. Scanning 21:246-52.
Zhang J, Ong SH, Le TM (2011). Kurtosis-based no-reference quality assessment of JPEG 2000 images. Sign Process: Image Comm 26:13-23.
Zhang X, Feng X, Wang W, Xue W (2013). Edge strength similarity for image quality assessment. IEEE Signal Process Lett 20:319-22.
Zhu T, Karam L (2014). A no-reference objective image quality metric based on perceptually weighted local noise. EURASIP J Image Video
Process 5, 8 pages.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Image Analysis & Stereology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
