nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Volumetric Texture Analysis Based on Three-Dimensional Gaussian Markov Random Fields for COPD Detection

verfasst von : Yasseen Almakady, Sasan Mahmoodi, Joy Conway, Michael Bennett

Erschienen in: Medical Image Understanding and Analysis

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper proposes a 3D GMRF-based descriptor for volumetric texture image classification. In our proposed method, the estimated parameters of the GMRF model in volumetric texture images are employed as texture features in addition to the mean of a processed image region. The descriptor of the volumetric texture is then constructed by computing the histograms of each feature element to characterize the local texture. The evaluation of this descriptor achieves a high classification accuracy on a 3D synthetic texture database. Our method is then applied on a clinical dataset to exploit its discriminatory power, achieving a high classification accuracy in COPD detection. To demonstrate the performance of the descriptor, a comparison is carried out against a 2D GMRF-based method using the same dataset, variables, and settings. The descriptor outperforms the 2D GMRF-based method by a significant margin.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study

Nächstes Kapitel Texture Descriptors for Classifying Sparse, Irregularly Sampled Optical Endomicroscopy Images

Murray, C.J.L., Lopez, A.D.: Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study. Lancet 349(9064), 1498–1504 (1997)CrossRef

Decramer, M., Janssens, W., Miravitlles, M.: Chronic obstructive pulmonary disease. Lancet 379(823), 1341–1351 (2012)CrossRef

Vogelmeier, C., Criner, G., et al.: A global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease report. GOLD executive summary. Am. J. Respir. Crit. Care Med. 195(5), 557–582 (2017)CrossRef

Sorensen, L., Shaker, S.B., De Bruijne, M.: Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans. Med. Imaging 29(2), 559–569 (2010)CrossRef

Müller, N.L., et al.: Density mask: an objective method to quantitate emphysema using computed tomography. Chest 94(4), 782–787 (1988)CrossRef

Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRef

Pena, I.P., et al.: Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images, arXiv preprint arXiv:1706.02051 (2017)

Heimann, T., Meinzer, H.-P.: Statistical shape models for 3D medical image segmentation: a review. Med. Image Anal. 13(4), 543–563 (2009)CrossRef

Guo, Z., et al.: Texture classification by texton: Statistical versus binary. PLoS ONE 9(2), e88073 (2014)CrossRef

10.

Xianghua, X., Mirmehdi, M.: A galaxy of texture features. In: Handbook of Texture Analysis, pp. 375–406 (2008)

11.

Mao, J., Jain, A.K.: Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recogn. 25(2), 173–188 (1992)CrossRef

12.

Park, Y.S., et al.: Texture-based quantification of pulmonary emphysema on high-resolution computed tomography: comparison with density-based quantification and correlation with pulmonary function test. Invest. Radiol. 43(6), 395–402 (2008)CrossRef

13.

Gao, X., et al.: Texture-based 3D image retrieval for medical applications. In: IADIS International Conference on e-Health (2010)

14.

Citraro, L., et al.: Extended three-dimensional rotation invariant local binary patterns. Image Vis. Comput. 62, 8–18 (2017)CrossRef

15.

Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recogn. 24(12), 1167–1186 (1991)CrossRef

16.

Toriwaki, J., Yoshida, H.: Fundamentals of Three-Dimensional Digital Image Processing. Springer, London (2009). https://doi.org/10.1007/978-1-84800-172-5CrossRefMATH

17.

Depeursinge, A., et al.: Three-dimensional solid texture analysis in biomedical imaging: review and opportunities. Med. Image Anal. 18(1), 176–196 (2014)CrossRef

18.

Dharmagunawardhana, C., et al.: Rotation invariant texture descriptors based on Gaussian Markov random fields for classification. Pattern Recogn. Lett. 69, 15–21 (2016)CrossRef

19.

Petrou, M., Sevilla, P.G.: Image Processing: Dealing with Texture, 1st edn. Wiley, Chichester (2006)CrossRef

20.

Genschel, U., William, Q.M.: A comparison of maximum likelihood and median-rank regression for Weibull estimation. Qual. Eng. 22(4), 236–255 (2010)CrossRef

21.

Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)CrossRef

22.

Dharmagunawardhana, C., et al.: Gaussian Markov random field based improved texture descriptor for image segmentation. Image Vis. Comput. 32(11), 884–895 (2014)CrossRef

23.

Liu, X., DeLiang, W.: Texture classification using spectral histograms. IEEE Trans. Image Process. 12(6), 661–670 (2003)CrossRef

24.

Björkström, A.: Ridge regression and inverse problems. Stockholm University, Department of Mathematics (2001)

25.

Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer Series in Statistics, vol. 1. Springer, New York (2001). https://doi.org/10.1007/978-0-387-84858-7CrossRefMATH

26.

Paulhac, L., Makris, P., Ramel, Y.-Y.: A solid texture database for segmentation and classification experiments. In: VISAPP, vol. 2 (2009)

27.

Xu, Y., et al.: MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies. IEEE Trans. Med. Imaging 25(4), 464–475 (2006)CrossRef

28.

Yao, J., et al.: Computer-aided diagnosis of pulmonary infections using texture analysis and support vector machine classification. Acad. Radiol. 18(3), 306–314 (2011)CrossRef

29.

Sorensen, L., et al.: Texture-based analysis of COPD: a data-driven approach. IEEE Trans. Med. Imaging 31(1), 70–78 (2012)MathSciNetCrossRef

30.

Tuceryan, M., Jain, A.K.: Texture analysis. In: Handbook of Pattern Recognition and Computer Vision, pp. 235–276 (1993)

31.

Manjunath, B.S., Rama, C.: Unsupervised texture segmentation using Markov’s random field models. IEEE Trans. Pattern Anal. Mach. Intell. 13(5), 478–482 (1991)CrossRef

32.

Rellier, G., et al.: Texture feature analysis using a Gauss-Markov model in hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 42(7), 1543–1551 (2004)CrossRef

33.

Zhao, Y., et al.: Classification of high spatial resolution imagery using improved Gaussian Markov random-field-based texture features. IEEE Trans. Geosci. Remote Sens. 45(5), 1458–1468 (2007)CrossRef

Titel: Volumetric Texture Analysis Based on Three-Dimensional Gaussian Markov Random Fields for COPD Detection
verfasst von: Yasseen Almakady
Sasan Mahmoodi
Joy Conway
Michael Bennett
Verlag: Springer International Publishing
Buch: Medical Image Understanding and Analysis
Print ISBN: 978-3-319-95920-7

Electronic ISBN: 978-3-319-95921-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-95921-4_16

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"