PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Measurement of the upper respiratory tract aerated space volume using the results of computed tomography

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the possibilities of quantitative analysis of results obtained from CT examination of organs and anatomical structures of the upper respiratory tract. The presented results of the analysis were obtained using proprietary software developed in the MATLAB 2018b environment (Image Processing toolbox). The software enables to visualize the original results of CT scan and, after evaluating the visible structures, enables to select the area to be subjected to quantitative analysis. After the initial identification of an area of interest requiring detailed diagnostics, its volume and the surface areas of individual cross-sections are calculated in the area separated for examinations. A graphical presentation of the analysis results - the surface areas of selected cross-sections possible to visualize in two- and three-dimensional space - enables quick analysis of changes in the examined region.
Rocznik
Strony
387--401
Opis fizyczny
Bibliogr. 35 poz., rys., tab., wykr.
Twórcy
autor
  • Military University of Technology, Institute of Optoelectronics, Gen. W. Urbanowicza 2, 00-908 Warsaw, Poland
  • Military University of Technology, Institute of Optoelectronics, Gen. W. Urbanowicza 2, 00-908 Warsaw, Poland
  • University of Warmia and Mazury, Department of Otolaryngology and Head and Neck Disease, Warszawska 30,10-832 Olsztyn, Poland
Bibliografia
  • [1] Pietzsch, J. (2017). The Nobel Prize in Physiology or Medicine 1979. Allan M. Cormack, Godfrey N. Hounsfield, Perspectives.
  • [2] Walecki, J., Zawadzki, M. (2006). Postępy w diagnostyce obrazowej w 2005 roku. Medycyna praktyczna, 7(8), 185-186.
  • [3] Flohr, T. (2013). Current Radiology Reports. 1(1), 52-63.
  • [4] Klingenbeck-Regn, K., Schaller, S., Flohr, T. (1999). Subsecond multi-slice computed tomography: basics and applications. European Journal of Radiology, 3(12), 110-124.
  • [5] McCollough, C.H., Zink, F.E. (1999). Performance evaluation of a multi-slice CT system. Med. Phys., 26(11), 2223-2230.
  • [6] Hu, H., He, H.D., Foley, W.D., Fox, S.H. (2000). Four multidetector-row helical CT: image quality and volume coverage speed. Radiology, 215(1), 55-62.
  • [7] Mori, S., Endo, M., Tsunoo, T., Susumu, K., Tanada, S., Aradate, H. (2004). Physical performance evaluation of a 256-slice CT-scanner for four-dimensional imaging. Med. Phys., 31(6), 1348-1356.
  • [8] Mori, S., Endo, M., Obata, T., Tsunoo, T., Susumu, K., Tanada, S. (2006). Properties of the prototype 256-row (cone beam) CT scanner. European Radiology, 16(9), 2100-2108.
  • [9] Mori, S., Kondo, C., Suzuki, N., Hattori, A., Kusakabe, M., Endo, M. (2006). Volumetric coronary angiography using the 256-detector row computed tomography scanner: comparison in vivo and in vitro with porcine models. Acta Radiology, 47(2), 186-191.
  • [10] Kido, T., Kurata, A., Higashino, H., Sugawara, Y., Okayama, H., Higaki, J., Anno, H., Katada, K., Mori, S., Tanada, S., Endo, M., Mochizuki, T. (2007). Cardiac imaging using 256-detector row four-dimensional CT: preliminary clinical report. Radiation Medicine, 25(1), 38-44.
  • [11] AW Volume Viewer. https://www.gehealthcare.com/en/products/advanced-visualization/all-applications/volume-viewer. (2019).
  • [12] RadiAnt. https://www.radiantviewer.com/pl/. (2019).
  • [13] Mimics. https://www.materialise.com/en/medical/software/mimics. (2019).
  • [14] Agacayak, K.S., Gulsun, B., Koparal, M., Atalay, Y., Aksoy, O., Adıgüzel, Ö. (2015). Alterations in Maxillary Sinus Volume among Oral and Nasal Breathers. Medical science monitor, 21, 18-26.
  • [15] Hassan, E., Aboshgifa, A. (2015). Detecting Brain Tumour from Mri Image Using Matlab GUI Programme. International Journal of Computer Science & Engineering Survey, 6(6), 47-60.
  • [16] Chu, C., Takaya, K. (1993). 3-Dimensional rendering of MR images. WESCANEX 93. Communications, Computers and Power in the Modern Environment Conference Proceedings, IEEE, 165-170.
  • [17] Clarke, L., Velthuizen, R., Camacho, M., Heine, J., Vaydianathan, M., Hall, L., Thatcher, R., Silbiger, M. (1995). MRI segmentation: Methods and applications. Magnetic Resonance Imaging, 13, 343-368.
  • [18] Leemput, K.V., Maes, F., Vandermeulen, D., Suetens, P. (1999). Automated model-based tissue classification of MR images of the brain. IEEE Trans. on Medical Imaging, 897-908.
  • [19] Norouzi, A., Shafry, M., Rahim, M., Altameem, A., Saba, T., Ehsani Rad, A., Rehman, A., Uddin, M. (2014). Medical Image Segmentation Methods, Algorithms, and Applications. IETE Technical Review, 31(3), 199-213.
  • [20] Lim, K., Pfefferbaum, A. (1989). Segmentation of MR Brain Images into Cerebrospinal Fluid Spaces, White and Gray matter. Journal of Computer Assisted Tomography, 13(4), 588-593.
  • [21] Lucas-Quesada, F.A., Sinha, U., Sinha, S. (1996). Segmentation strategies for breast tumors from dynamic MR images. J. Magn. Reson. Imaging, 6(5), 753-763.
  • [22] Davies, E. (2005). Machine Vision: Theory, Algorithms, Practicalities. San Francisco, CA: Morgan Kaufmann.
  • [23] Sonka, M., Hlavac, V., Boyle, R. (1999). Image processing analysis and machine vision. 2nd ed. Pacific Grove, CA: PWS Publishing, 123-133.
  • [24] Norouzi, A., Rahman, A., Shafry, M., Saba, T. (2012). Visualization and segmentation. Int. J. Acad. Res., 4(2), 202-208.
  • [25] Dalvi, R., Abugharbieh, R., Wilson, D., Wilson, D.R. (2007). Multi-contrast MR for enhanced bone imaging and segmentation. Conf. Proc. IEEE Eng. Med. Biol. Soc., 5620-5623.
  • [26] Rad, E.A., Rahim, M.S.M., Rehman, A., Altameem, A., Saba, T. (2013). Evaluation of Current Dental Radiographs Segmentation Approaches in Computer-aided Applications. IETE Tech. Rev., 30(3), 210-222.
  • [27] Pham, D.L., Xu, C., Prince, J.L. (1998). A Survey of Current Methods in Medical Image Segmentation. Ann. Rev. Biomed. Eng., 2, 315-337.
  • [28] Nguyen, N., Laurendeau, D., Branzan-Albu, A. (2007). A new segmentation method for MRI images of the shoulder joint. Fourth Canadian IEEE Conference on Computer and Robot Vision (CRV’07), Montreal, 329-338.
  • [29] Kallergi, M., Woodsa, K., Clarkea, L., Qiana, W. (1992). Image segmentation in digital mammography: Comparison of local thresholding and region growing algorithms. Computerized Medical Imaging and Graphics, 16(5), 323-331.
  • [30] Olivera, A., Freixeneta, J., Martia, J., Pérezb, E., Pontb, J., Dentonc, E., Zwiggelaard, R. (2010). A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal., 14(2), 87-110.
  • [31] Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer, 1-5.
  • [32] Theodoridis, S., Pikrakis, A., Koutroumbas, K., Cavouras, D. (2010). Introduction to Pattern Recognition: A Matlab Approach. Burlington. VT: Academic Press.
  • [33] Wells, W. Grimson, W., Kikins, R., Jolesz, F. (1996). Adaptive segmentation of MRI data. IEEE Trans. Med. Imag., 15(4), 429-442.
  • [34] Mantas, J. (1987). Methodologies in pattern recognition and image analysis - A brief survey. Pattern Recognition, 22(1), 1-6.
  • [35] Ababneh, S.Y., Prescott, J.W., Gurcan, M.N. (2011). Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research. Med. Image Anal., 15(4), 438-448.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-30ba307e-46fb-44dc-b4ab-4f763befea98
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.