A Preliminary Comparison of Two Different Methods for Objective Uniformity Evaluation in Diagnostic Ultrasound Imaging

  • Andrea Scorza
  • Silvia Conforto
  • Maurizio Schmid
  • Daniele Bibbo
  • Salvatore Andrea Sciuto
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 57)

Abstract

Although ultrasound image uniformity is a very important parameter for quality assurance in diagnostic ultrasounds, it is usually assessed by a qualitative and subjective judgement of technicians. In this work two novel method to obtain an objective measurement of the B-mode image uniformity over the whole field of view (or some of its part) are briefly described and compared: with the aim to quantify non-uniformities the first method is based on the image gray level histogram weighted by a sigmoid function (Sigma Weighted Histogram Method or SWHM) while the second one applies a segmentation of the Region of Interest, depending on some texture features from co-occurrence matrices processing (Texture Distribution Analysis Method or TDAM). Results from the two methods are preliminary compared and discussed on a set of 9 test images provided by means of two commercial ultrasound phantoms applied on different ultrasound scanners: the sensitivity to non-uniformities in SWHM is lower than in TDAM, on the other hand TDAM scores are more unstable and affected by higher uncertainties due to co-occurrence matrices calculations. Both methods require improvements and an in depth validation testing, nevertheless results are encouraging.

Keywords

Uniformity measurement Diagnostic ultrasound Quality assurance Texture Co-occurrence 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    N. Dudley, S. Russell, B. Ward and P. Hoskins (2014) The BMUS guidelines for regular quality assurance testing of ultrasound scanners. Ultrasound, 22: 6-7.Google Scholar
  2. 2.
    N. Dudley, S. Russell, B. Ward and P. Hoskins (2014) BMUS guidelines for the regular quality assurance testing of ultrasound scanners by sonographers. Ultrasound, 22: 8 -14Google Scholar
  3. 3.
    W.R. Hedrik et al. (2005) Ultrasound Physics and Instrumentation, Mosby.Google Scholar
  4. 4.
    A. Scorza, G. Lupi, S. A. Sciuto, L. Battista, J. Galo (2015) A preliminary study on a method for objective uniformity assessment in diagnostic ultrasound imaging. In: 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2015) Proceedings, Pisa, Italy, pp. 1628-1633Google Scholar
  5. 5.
    A. Scorza, S. Conforto, G. Lupi and S. A. Sciuto (2015) A texture analysis approach for objective uniformity evaluation in diagnostic ultrasound imaging: a preliminary study. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015) Proceedings, Milano, Italy, pp. 6317-6320Google Scholar
  6. 6.
    F. P. Branca, S. A. Sciuto, and A. Scorza (2012), Comparative evaluation of ultrasound scanner accuracy in distance measurement, Review of Scientific Instruments, vol. 83, 10.Google Scholar
  7. 7.
    Sung-Min Sohn, L. DelaBarre, A. Gopinath, J.T. Vaughan (2014) RF Head Coil Design With Improved RF Magnetic Near-Fields Uniformity for Magnetic Resonance Imaging (MRI) Systems. IEEE Transactions on Microwave Theory and Techniques, Vol 62, 8.Google Scholar
  8. 8.
    A. Fouad, T. J. Pfefer, C.W. Chen, W. Gong, A. Agrawal, P. H. Tomlins, P. D. Woolliams, R. A. Drezek, and Y. Chen (2014) Variations in optical coherence tomography resolution and uniformity: a multi-system performance comparison. Biomedical Optics Express, Vol. 5, Issue 7, pp. 2066-2081.Google Scholar
  9. 9.
    A. Scorza, L. Battista, S. Silvestri and S. A. Sciuto (2014) Design and development of a rheometer for biological fluids of limited availability. Review of Scientific Instruments, vol. 85, p. 105105.Google Scholar
  10. 10.
    L. Battista L., A. Scorza and S.A. Sciuto (2014) Fiber-Optic Flow Sensor for the Measurement of Inspiratory Efforts in Mechanical Neonatal Ventilation. Sensors and Microsystems, Lecture Notes in Electrical Engineering, Springer International Publishing, vol. 268, pp. 453-457.Google Scholar
  11. 11.
    D. Torricelli, S. Conforto, M. Schmid, T. D’Alessio (2008) A neural-based remote eye gaze tracker under natural head motion. Computer Methods and Programs in Biomedicine, 92 (1), pp. 66-78Google Scholar
  12. 12.
    L. Battista, S. A. Sciuto, and A. Scorza (2011) Preliminary evaluation of a fiber-optic sensor for flow measurements in pulmonary ventilators. In: Proceedings of MeMeA 2011 - 2011 IEEE International Symposium on Medical Measurements and Applications.Google Scholar
  13. 13.
    C. De Marchis, M. Schmid, D. Bibbo, I. Bernabucci, S. Conforto (2013) Inter-individual variability of forces and modular muscle coordination in cycling: A study on untrained subjects. Human Movement Science, 32 (6), pp. 1480-1494.Google Scholar
  14. 14.
    C. De Marchis, A. M. Castronovo,, D. Bibbo, M. Schmid., S. Conforto. (2012) Muscle synergies are consistent when pedaling under different biomechanical demands (2012) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, art. no. 6346672, pp. 3308-3311.Google Scholar
  15. 15.
    S. Conforto, S. A. Sciuto, D. Bibbo, A. Scorza. (2008) Calibration of a measurement system for the evaluation of efficiency indexes in bicycle training. IFMBE Proceedings, 22, pp. 106-109.Google Scholar
  16. 16.
    L Battista, A. Scorza, S. A. Sciuto (2012) Experimental characterization of a novel fiber-optic accelerometer for the quantitative assessment of rest tremor in parkinsonian patients. In: 9th IASTED International Conference of Biomedical Engineering (BioMed 2012), Innsbruck, Austria, pp.437 - 442.Google Scholar
  17. 17.
    Castronovo, A.M., De Marchis, C., Bibbo, D., Conforto, S., Schmid, M., D’Alessio, T. (2012) Neuromuscular adaptations during submaximal prolonged cycling Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, art. no. 6346748, pp. 3612-3615.Google Scholar
  18. 18.
    Fida, B., Bernabucci, I., Bibbo, D., Conforto, S., Schmid, M.(2015) Pre-processing effect on the accuracy of event-based activity segmentation and classification through inertial sensor. Sensors (Switzerland), 15 (9), art. no. 105, pp. 23095-23109.Google Scholar
  19. 19.
    Fida, B., Bernabucci, I., Bibbo, D., Conforto, S., Schmid, M. (2015) Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer. Medical Engineering and Physics, 37 (7), pp. 705-711.Google Scholar
  20. 20.
    M.M. Goodsitt et al. (1998) Real-time B-mode ultrasound quality control test procedures Report of AAPM Ultrasound Task Group No. 1. Med. Phys., Vol. 25, 8Google Scholar
  21. 21.
    F.P. Branca (2008) Fondamenti di Ingegneria Clinica vol.2 - Ecotomografi, Springer.Google Scholar
  22. 22.
    H. Lopez, C. M. Kimme-Smith, J. Ophir, J. D. Satrapa, B. S. Garra, J. A. Zagzebski and K. E. Thomenius (1995) Methods for measuring performance of Pulse-Echo Ultrasound Equipment-part II: digital methods (stage 1). American institute of Ultrasound in Medicine.Google Scholar
  23. 23.
    E. L. Madsen, B. S. Garra, J. A. Parks, A. C. Skelly, and J. A. Zagzebski (1995) AIUM Quality Assurance Manual for Gray-Scale Ultrasound Scanners (Stage 2). American Institute of Ultrasound in Medicine, Laurel, MD.Google Scholar
  24. 24.
    A. Scorza (2009) A novel method for automatic evaluation of the effective dynamic range of medical ultrasound scanners. In: 4th European Conference of the International Federation for Medical and Biological Engineering, IFMBE Proceedings, Antwerp, Vol. 22, pp 1607-1611.Google Scholar
  25. 25.
    F. Marinozzi, F.P. Branca, F. Bini, A. Scorza (2012) Calibration procedure for performance evaluation of clinical Pulsed Doppler Systems. Measurement, vol. 45, pp. 1334-1342Google Scholar
  26. 26.
    F. Marinozzi, F. Bini, A. D’Orazio, A. Scorza (2008) Performance Tests of Sonographic Instruments for the Measure of Flow Speed. In: 2008 IEEE International Workshop on Imaging Systems and Techniques. Chania, Greece, 2008Google Scholar
  27. 27.
    A. Scorza, G. Lupi, S. A. Sciuto, F. Bini, F. Marinozzi (2015) A novel approach to a phantom based method for maximum depth of penetration measurement in diagnostic ultrasound: a preliminary study. In: 2015 IEEE International Symposium on Medical Measurements and Applications (MEMEA 2015) Proceedings, Torino, Italy, pp.369-374.Google Scholar
  28. 28.
    A. Scorza, S. Conforto, C. d’Anna and S. A. Sciuto (2015) A comparative study on the influence of probe placement on quality assurance measurements in B-mode Ultrasound by means of ultrasound phantoms, The Open Journal of Biomedical Engineering, 9, 164-178Google Scholar
  29. 29.
    C. Kollman, C. de Korte, N.J. Dudley et al. (2012) Guideline for technical quality assurance (TQA) of ultrasound devices (B-Mode) - Version 1.0. Ultraschall Med 33:544-9Google Scholar
  30. 30.
    AAVV (1994) The QA Cookbook for Ultrasound” Gammex Inc,Google Scholar
  31. 31.
    M.D. Levine, A. M. Nazif (1985) Dynamic Measurement of Computer Generated Image Segmentations. IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. PAMI-7, n°2, 155-Google Scholar
  32. 32.
    AAVV (2004) General Purpose Ultrasound Phantom - model 054. Computerized Imaging Reference Systems Inc.Google Scholar
  33. 33.
    AAVV (2013) Small Parts Ultrasound Phantom - model 050. Computerized Imaging Reference Systems Inc.Google Scholar
  34. 34.
    R. M. Haralick, K. Shanmugam, Its’Hak Dinstein (1973) Textural Features for Image Classification. IEEE Trans. Syst. Man. Cybernet. SMC-3, 610-621Google Scholar
  35. 35.
    R. M. Haralick (1979) Statistical and structural approaches to texture” Proc. IEEE 67, 786-804Google Scholar
  36. 36.
    S. Mukherjee, A Chakravorty, K. Ghosh, M. Roy1, A. Adhikari and S. Mazumdar (2007) Corroborating the subjective classification of ultrasound images of normal and fatty human livers by the radiologist through texture analysis and SOM. IEEE, 15th International Conference on Advanced Computing and Communications.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrea Scorza
    • 1
  • Silvia Conforto
    • 1
  • Maurizio Schmid
    • 1
  • Daniele Bibbo
    • 1
  • Salvatore Andrea Sciuto
    • 1
  1. 1.Department of EngineeringRoma Tre UniversityRomeItaly

Personalised recommendations