Abstract
This paper aims to two main goals, first goal is to achieve the characterization of quality control of ultrasound scanners based on the potential image metrics. On the other hand, the most effective goal is how to classify ultrasound scanners based on image metrics to evaluate performance of ultrasound transducer. The authors utilize the metrics to give information about the spatial arrangement of the gray levels in the specific interest region. The execution of ultrasound images metric based on a set of 19 metrics (i.e. contrast, gradient and Laplacian). This set reflects quality control of ultrasound scanners. The wok of this paper based on the best 6 metrics from 19 metrics which extracted from linear discriminative analysis (LDA). The classification methods used for minimum numbers of metrics are fused using support vector machine (SVM) and the highest classification method is back propagation neural network (BPNN) classifiers to get the main target of paper. Finally, the results show that objective performance evaluation of ultrasound transducer accuracy was 100% by using back propagation neural network classifier.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hall, T.J., Insana, M.F., Harrison, L.A., et al.: Ultrasound contrast detail analysis: a comparison of low contrast detectability among scanhead designs. J. Med. Phys. 22, 1117–1125 (1995)
Hoskins, P., Martin, K., Thrush, A.: Diagnostic Ultrasound: Physics and Equipment, p. 147. Cambridge University Press, Cambridge (2010)
Zdero, R., Fenton, P.V., Bryant, J.T.: A digital image analysis method for diagnostic ultrasound calibration. Ultrasonics 39, 695–702 (2002)
Lopez, H., Loew, M.H., Goodenough, D.J.: Objective analysis of ultrasound images by use of a computational observer. IEEE Trans. Med. Imaging 11, 496–506 (1992)
Rownd, J.J., Madsen, E.L., Zagzebski, J.A., et al.: Phantoms and automated system for testing the resolution of ultrasound scanners. J. Ultrasound Med. Biol. 23, 245–260 (1997)
Kofler, J.M., Madsen, E.L.: Improved method for determining resolution zones in ultrasound phantoms with spherical simulated lesions. Ultrasound Med. Biol. 27, 1667–1676 (2001)
Kofler, J.M., Lindstrom, M.J., Kelcz, F., et al.: Association of automated and human observer lesion detecting ability using phantoms. J. Ultrasound Med. Biol. 31, 351–359 (2005)
Thijssen, J.M., Weijers, G., De Korte, C.L.: Objective performance testing and quality assurance of medical ultrasound equipment. J. Ultrasound Med. Biol. 33, 460–471 (2007)
Rosenfeld, E., Wolter, S., Kopp, A., et al.: Investigation of the suitability of tissue phantoms for testing the constancy of ultrasonic transducer arrays in quality assurance. Ultraschall Med. 33, 289–294 (2012)
Rosenfeld, E., Jenderka, K.V., Kopp, A., et al.: How perfect are you with defective probes? Information on the results of the mini-trial on technical quality assurance during the “Ultraschall 2012” conference in Davos. Ultraschall Med. 34, 185–188 (2013)
Weigang, B., Moore, G.W., Gessert, J., et al.: The method and effects of transducer degradation on image quality and the clinical efficacy of diagnostic sonography. J. Diagn. Med. Sonogr. 19, 3–13 (2003)
Cozzolino, P., Stramare, R., Udilano, A., et al.: Quality control of ultrasound transducers: analysis of evaluation parameters and results of a survey of 116 transducers in a single hospital. Radiol. Med. (Torino) 115, 668–677 (2010)
Hangiandreou, N.J., Stekel, S.F., Tradup, D.J., et al.: Four-year experience with a clinical ultrasound quality control program. Ultrasound Med. Biol. 37, 1350–1357 (2011)
Wolter, S., Kopp, A., Liebscher, E., et al.: Consistency check of diagnostic ultrasound transducer arrays using tissue equivalent Phantoms. AIP Conf. Proc. 1433, 644–647 (2012)
Filho, A.C., Rodrigues, E.P., Junior, J.E., et al.: A computational tool as support in B-mode ultrasound diagnostic quality control, Master dissertation. Braz. J. Biomed. Eng. 30, 402–405 (2014)
Gonzalez, R.C.: Digital Image Processing, pp. 125–128. Prentice Hall, Prentice (2002)
Satonkar, S.S., Kurhe, A.B., Khanale, P.B.: Face recognition using principal component analysis and linear discriminant analysis on holistic approach in facial images database. J. Eng. 2, 15–23 (2012)
Singh, M., Singh, S., Gupta, S.: An information fusion-based method for liver classification using image metrics of ultrasound images. Inf. Fusion 19, 91–96 (2014)
Jaffe, C.C., Harris, D.J., Taylor, K.J.W., Viscomi, G., Mannes, E.: Sonographic transducer performance cannot be evaluated with clinical images. Am. J. Roentgenol. 137, 1239–1243 (1981)
Lualdi, M., Gamberale, L., Pignoli, E.: A novel computerized method for quality assurance of medical ultrasound probes. Phys. Med. 32, 81 (2016)
Montani, L., Paoli, M., Camarda, M., et al.: Implementation of a quality assurance program for ultrasound transducers. Phys. Med. 32, 136 (2016)
Goodsitt, M.M., Carson, P.L., Witt, S., et al.: Real-time B-mode ultrasound quality control test procedures. Report of AAPM Ultrasound Task Group No. 1. J Med. Phys. 25, 1385–1406 (1998)
Kalyan, K., Lele, R.D., Jakhia, B., et al.: Artificial neural network application in the diagnosis of disease conditions with liver ultrasound images. Adv. Bioinform. 2014, 1–14 (2014)
Price, J.H., Gough, D.A.: Comparison of phase-contrast and fluorescence digital autofocus for scanning microscopy. J. Cytom. 16, 283–297 (1994)
Mabrouk, M., Karrar, A., Sharawy, A.: Computer aided detection of large lung nodules using chest computer tomography image. Int. J. Appl. Inf. Syst. 3, 12–18 (2012)
Gonzalez, R.C.: Digital Image Processing Using Matlab, pp. 601–607. Mc Graw Hill Education, London (2009)
Abduh, Z., Abdel Wahed, M.A., Kadah, Y.M.: Robust computer-aided detection of pulmonary nodules from chest computed tomography. J. Med. Imaging Health Inf. 6, 1–7 (2016)
Javed, U., Riaz, M.M., Cheema, T.A.: MRI brain classification using texture features, fuzzy weighting and support vector machine. J. Progr. Electromagn. Res. B 53, 73–88 (2013)
McGee, K.P., Manduca, A., Felmlee, J.P., et al.: Image metric-based correction (autocorrection) of motion effects: analysis of image metrics. J. Magn. Resonan. Imaging 11, 174–181 (2000)
Vollath, D.: The influence of the scene parameters and of noise on the behavior of automatic focusing algorithms. J. Microsc. 151, 133–146 (1988)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sharawy, A.A., Mohammed, K.K., Aouf, M., Salem, M.AM. (2019). Ultrasound Transducer Quality Control and Performance Evaluation Using Image Metrics. In: Hassanien, A., Tolba, M., Shaalan, K., Azar, A. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. AISI 2018. Advances in Intelligent Systems and Computing, vol 845. Springer, Cham. https://doi.org/10.1007/978-3-319-99010-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-99010-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99009-5
Online ISBN: 978-3-319-99010-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)