Automatic Placement of Regions of Interest using Distance transform to Measure Spatial Resolution on the Clinical Computed Tomography Images : A Pilot Study

Authors

  • Ulil A. Taufiq  Departement of Physics, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
  • Choirul Anam  Departement of Physics, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
  • Eko Hidayanto  Departement of Physics, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
  • Ariij Naufal  Departement of Physics, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia

DOI:

https://doi.org//10.32628/IJSRST229653

Keywords:

CT, distance transform, region of interest, orthogonality, spatial resolution

Abstract

We propose a new algorithm called distance transform region of interest (DT-ROI) to deal with the irregular patient's surface. The ROIs can be placed orthogonally along the patient’s surface to get spatial resolution. The algorithm was developed using several image processing techniques. The original image was first segmented to obtain a segmented image. The segmented image was eroded and dilated to obtain an eroded and dilated image. Both the eroded and dilated images were edge detected to obtain the edge images of the eroded and dilated image. The edge images were distance transformed to obtain the closest pixel coordinate. Finally, ROIs were placed based on the coordinates obtained before. The DT-ROI was then assessed qualitatively by comparison with the ROI placement from the standard radial ROI (SR-ROI) on a Polymethyl methacrylate (PMMA) phantom, an anthropomorphic phantom, and the patient’s computed tomography images. The algorithm resulted in orthogonalized ROIs, both along the irregular object and the circular object. The ROI comparison between DT-ROI and SR-ROI shows a little difference in terms of orthogonality on PMMA phantom. Meanwhile, on the anthropomorphic phantom and the patient’s CT image, the DT-ROI produced a lot more orthogonal ROIs than the SR-ROI. Several ROIs of the DT-ROI have decreased orthogonality at certain sections, which can be observed in both phantom and patient images. However, theoretically, a slight decrease in orthogonality will not affect the modulation transfer function (MTF) measurement significantly. The DT-ROI algorithm has been successfully developed based on distance transformation and performed as the design. The algorithm can automatically place ROIs along the patient’s irregular surface better than the SR-ROI algorithm. However, not all ROIs placed from DT-ROI are well-orthogonalized. DT-ROI still needs to be improved before it is used to measure MTF to obtain a more optimal measurement.

References

  1. United Nations Scientific Committee on the Effects of Atomic Radiation. Sources, Effects and Risks of Ionizing Radiation, United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) 2020/2021 Report, Volume I: Report to the General Assembly, with Scientific Annex A-Evaluation of Medical Exposure to Ionizing Radiation. United Nations; 2022.
  2. Pauwels R, Silkosessak O, Jacobs R, Bogaerts R, Bosmans H, Panmekiate S. A pragmatic approach to determine the optimal kvp in cone beam CT: Balancing contrast-to-noise ratio and radiation dose. Dentomaxillofacial Radiology. 2014;43(5):20140059.
  3. Gascho D, Thali MJ, & Niemann T. Post-mortem computed tomography: technical principles and recommended parameter settings for high-resolution imaging. Medicine, Science and the Law. 2018;58(1):70-82.
  4. Tolentino EdeS, Amoroso-Silva PA, Alcalde MP, et al. Accuracy of high-resolution small-volume cone-beam computed tomography in detecting complex anatomy of the apical isthmi: ex vivo analysis. Journal of Endodontics. 2018;44(12):1862-1866.
  5. INTERNATIONAL ATOMIC ENERGY AGENCY. Quality Assurance Programme for Computed Tomography: Diagnostic and Therapy Applications. In IAEA Human Health Series No. 19. IAEA: Vienna. 2012.
  6. González‐López A, Campos‐Morcillo PA, & Lago‐Martín JD. An oversampling procedure to calculate the MTF of an imaging system from a bar‐pattern image. Medical Physics. 2016:43(10);5653-5658.
  7. Anam C, Fujibuchi T, Budi WS, Haryanto F, Dougherty G. An algorithm for automated modulation transfer function measurement using an edge of a PMMA phantom: Impact of field of view on spatial resolution of CT images. Journal of Applied Clinical Medical Physics. 2018:19(6);244-252.
  8. Rueckel J, Stockmar M, Pfeiffer F, Herzen J. Spatial resolution characterization of a X-ray microCT system. Applied Radiation and Isotopes. 2014;94:230-234.
  9. Manson EN, Bambara L, Nyaaba RA, et al. Comparison of Modulation Transfer Function Measurements for Assessing The Performance of Imaging Systems. Medical Physics. 2017;5(2):188-191.
  10. Ainurrofik N, Anam C, Sutanto H, Dougherty G. An automation of radial modulation transfer function (MTF) measurement on a head polymethyl methacrylate (PMMA) phantom. AIP Conference Proceedings. 2021;2346 (1):040009.
  11. González‐López A. Effect of noise on MTF calculations using different phantoms. Medical Physics. 2018;45(5):1889-1898.
  12. Maruyama S. Assessment of Uncertainty Depending on Various Conditions in Modulation Transfer Function Calculation Using the Edge Method. J Med Phys. 2021;46(3):221-227.
  13. Kayugawa A, Ohkubo M, & Wada S. Accurate determination of CT point‐spread‐function with high precision. Journal of Applied Clinical Medical Physics. 2013;14(4):216-226.
  14. Xie X, Fan H, Wang A, Zou N, & Zhang Y. Regularized slanted-edge method for measuring the modulation transfer function of imaging systems. Applied optics. 2018;57(22):6552-6558.
  15. Brunner CC, Stern SH, Minniti R, Parry MI, Skopec M, & Chakrabarti K. CT head‐scan dosimetry in an anthropomorphic phantom and associated measurement of ACR accreditation‐phantom imaging metrics under clinically representative scan conditions. Medical Physics. 2013;40(8):081917.
  16. Bor D, Unal E, & Uslu A. Comparison of different phantoms used in digital diagnostic imaging. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2015;795:160-166.
  17. Gay F, Pavia Y, Pierrat N, Lasalle S, Neuenschwander S, & Brisse HJ. Dose reduction with adaptive statistical iterative reconstruction for paediatric CT: phantom study and clinical experience on chest and abdomen CT. European Radiology. 2014;24(1):102-111.
  18. Cheng Y, Abadi E, Smith TB, et al. Validation of algorithmic CT image quality metrics with preferences of radiologists. Medical Physics. 2019;46(11):4837-4846.
  19. Ahmad M, Jacobsen MC, Thomas MA, Chen HS, Layman RR, & Jones AK. A Benchmark for automatic noise measurement in clinical computed tomography. Medical Physics. 2021;48(2):640-647.
  20. Chun M, Choi JH, Kim S, Ahn C, & Kim JH. Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study. PloS one. 2022;17(7):e0271724.
  21. Christianson O, Winslow J, Frush DP, Samei E. Automated Technique to Measure Noise in Clinical CT Examinations. AJR Am J Roentgenol. 2015;205(1):W93-W99.
  22. Malkus A, Szczykutowicz TP. A method to extract image noise level from patient images in CT. Medical Physics. 2017;44(6):2173-2184.
  23. Anam C, Budi WS, Fujibuchi T, Haryanto F, & Dougherty G. Validation of the tail replacement method in MTF calculations using the homogeneous and non-homogeneous edges of a phantom. Journal of Physics: Conference Series. 2019;1248(1):012001.
  24. Li K, Garrett J, Ge Y, & Chen GH. Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance. Medical physics. 2014;41(7):071911.
  25. Sanders J, Hurwitz L, & Samei E. Patient‐specific quantification of image quality: an automated method for measuring spatial resolution in clinical CT images. Medical Physics. 2016;43(10):5330-5338.
  26. Shih FY. Image processing and mathematical morphology: fundamentals and applications. CRC Press; 2017.
  27. Li F, Zlatanova S, Koopman M, Bai X, & Diakité A. Universal path planning for an indoor drone. Automation in Construction. 2018;95:275-283.
  28. Baum D, Weaver JC, Zlotnikov I, Knötel D, Tomholt L, & Dean MN. High-throughput segmentation of tiled biological structures using random-walk distance transforms. Integrative and Comparative Biology. 2019;59(6):1700-1712.
  29. Elizondo-Leal JC, Ramirez-Torres JG, Barrón-Zambrano JH, Diaz-Manríquez A, Nuño-Maganda MA, & Saldivar-Alonso VP. Parallel raster scan for Euclidean distance transform. Symmetry. 2020;12(11):1808.
  30. Marasca A, Backes A, Favarim F, Teixeira M, & Casanova D. EDT method for multiple labelled objects subject to tied distances. International Journal of Automation and Computing. 2021;18(3):468-479.
  31. Wang J, & Tan Y. Efficient Euclidean distance transform algorithm of binary images in arbitrary dimensions. Pattern Recognition. 2013;46(1):230-242.
  32. Abdalla M, & Nagy B. Dilation and erosion on the triangular tessellation: an independent approach. IEEE Access. 2018;6:23108-23119.
  33. Deng L, Zhang J, Xu G, & Zhu H. Infrared small target detection via adaptive M-estimator ring top-hat transformation. Pattern Recognition. 2021;112:107729.
  34. Fuseiller G, Marie R, Mourioux G, Duno E, & Labbani-Igbida O. Enhancing distance transform computation by leveraging the discrete nature of images. Journal of Real-Time Image Processing. 2022;19:763-773.
  35. Akkoul S, Hafiane A, Rozenbaum O, Lespessailles E, & Jennane R. 3D Reconstruction of the proximal femur shape from few pairs of x-ray radiographs. Signal Processing: Image Communication. 2017;59:65-72.
  36. Xie X, Fan H, Wang H, Wang Z, & Zou N. Error of the slanted edge method for measuring the modulation transfer function of imaging systems. Applied Optics. 2018;57(7):B83-B91.
  37. Narita A, Ohkubo M, Fukaya T, & Noto Y. A simple method for measuring the slice sensitivity profile of iteratively reconstructed CT images using a non‐slanted edge plane. Medical Physics. 2021;48(3):1125-1130.
  38. Kajihara Y, Fukuzawa K, Itoh S, Watanabe R, & Zhang H. Theoretical and experimental study on two-stage-imaging microscopy using ellipsometric contrast for real-time visualization of molecularly thin films. Review of Scientific Instruments. 2013;84(5):053704.
  39. Tsai YW, Chu CH, Shih WH, Jin SC, Chen JC, & Liang,KC. Evaluation of different modulation transfer function measurement based on different edge spread function calculations. Journal of Medical and Biological Engineering. 2019;39(6):901-911.
  40. Xiang C, Chen X, Chen Y, Zhou J, & Shen W. MTF measurement and imaging quality evaluation of digital camera with slanted-edge method. Optical Design and Testing IV. 2010;7849:85-92.
  41. Estribeau M, & Magnan P. Fast MTF measurement of CMOS imagers using ISO 12333 slanted-edge methodology. Detectors and Associated Signal Processing. 2004;5251:243-252.

International Journal of Scientific Research in Science and Technology

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Published

2022-12-30

Issue

Volume 9, Issue 6, November-December-2022

Section

Research Articles

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How to Cite

[1]
Ulil A. Taufiq, Choirul Anam, Eko Hidayanto, Ariij Naufal, " Automatic Placement of Regions of Interest using Distance transform to Measure Spatial Resolution on the Clinical Computed Tomography Images : A Pilot Study, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 6, pp.462-471, November-December-2022. Available at doi : https://doi.org/10.32628/IJSRST229653