Pattern classification of interstitial lung disease in high resolution clinical datasets: A systematic review

  • Authors

    • S Ummay Atiya
    • N V.K Ramesh
    https://doi.org/10.14419/ijet.v7i2.7.10275

    Received date: March 18, 2018

    Accepted date: March 18, 2018

    Published date: March 18, 2018

  • Automated tissues characterization, Deep Convolution Neural Network, ground glass, Interstitial lung diseases, High Resolution Computed Tomography,

  • Abstract

    Automated tissues characterization helps to diagnosis the various diseases including Interstitial lung diseases (ILD). The various features and the several classifiers are used in categorize the different layers depend on the pattern presented in the image. The different types of diseases may occur in the lungs and some of the diseases happen to leave the scars. These scars can be found in the High Resolution Computed Tomography (HRCT) and have different pattern. The different diseases cause the different pattern in the images and these is classified using the efficient classifier that helps to diagnosis the diseases. In this paper, review for the many researches regarding to the classification of the different pattern from the Computed Tomography (CT) images is presented. The evaluation of the efficiency of the methods in terms of classifier and database used for the research is made. The Deep Convolution Neural Network (CNN) provides the promising classifier efficiency compared to the other researches for different pattern. In general, there are five types of pattern is classified: Healthy, ground glass, honeycomb, Fibrosis, and emphysema.

  • References

    1. Korfiatis PD, Kalogeropoulou C, Karahaliou AN, Kazantzi AD & Costaridou LI, “Vessel tree segmentation in presence of inter-stitial lung disease in MDCT”, IEEE Transactions on Information Technology in Biomedicine, Vol. 15, No. 2, pp. 214-220, 2011.
    2. Okumura E, Kawashita I & Ishida T, “Computerized Classifica-tion of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages”, Journal of digital imaging, Vol. 30, No. 4, pp. 413-426, 2017.
    3. Depeursinge A, Pad P, Chin AS, Leung AN, Rubin DL, Müller H & Unser M, “Optimized steerable wavelets for texture analysis of lung tissue in 3-D CT: Classification of usual interstitial pneu-monia”, Proceedings of the 12th International Symposium on In Biomedical Imaging (ISBI), pp. 403-406, 2015.
    4. Dash JK, Mukhopadhyay, S, Garg MK, Prabhakar N & Khandelwal N, “Multi-classifier framework for lung tissue classi-fication”, Proceedings of the Students' Technology Symposium (TechSym), pp. 264-269, 2014.
    5. Depeursinge A, Van de Ville D, Platon A, Geissbuhler A, Poletti PA and Muller H, “Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames”, IEEE Transac-tions on Information Technology in Biomedicine, Vol. 16, No. 4, pp.665-675, 2012.
    6. Song Y, Cai W, Zhou Y & Feng DD, “Feature-based image patch approximation for lung tissue classification”, IEEE transac-tions on medical imaging, Vol. 32, No. 4, pp. 797-808, 2013.
    7. Kockelkorn TT, Sánchez CI, Grutters JC, Ramos R, de Jong PA, Viergever MA, Ramos J, Schaefer-Prokop C & van Ginneken B, “Interactive classification of lung tissue in CT scans by combin-ing prior and interactively obtained training data: A simulation study”, Proceedings of the 21st International Conference on Pat-tern Recognition (ICPR), pp. 105-108, 2012.
    8. Depeursinge A, Vargas A, Platon A, Geissbuhler A, Poletti PA & Müller H, “3D case–based retrieval for interstitial lung diseases”, Proceedings of the MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, pp. 39-48, 2009.
    9. Vogl WD, Prosch H, Müller-Mang C, Schmidt-Erfurth U & Langs G, “Longitudinal alignment of disease progression in fi-brosing interstitial lung disease”, Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 97-104, 2014.
    10. Fetita C, Chang-Chien KC, Brillet PY, Prêteux F & Grenier P, “Diffuse parenchymal lung diseases: 3D automated detection in MDCT”, Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 825-833, 2007.
    11. Vo KT & Sowmya A, “Directional multi-scale modeling of high-resolution computed tomography (hrct) lung images for diffuse lung disease classification”, Proceedings of the International Con-ference on Computer Analysis of Images and Patterns, pp. 663-671, 2009.
    12. Gupta RD, Dash JK & Mukhopadhyay S, “Content based re-trieval of interstitial lung disease patterns using spatial distribu-tion of intensity, gradient magnitude and gradient direction”, Proceedings of the International Conference on Systems in Medi-cine and Biology (ICSMB), pp. 58-61, 2016.
    13. Hamzah MFM, Kasim RM, Yunus A, Rijal OM & Noor NM, “Detection of Interstitial Lung Disease using correlation and re-gression methods on texture measure. Proceedings of the IEEE International Conference on Imaging, Vision & Pattern Recogni-tion (icIVPR), pp. 1-4, 2017.
    14. Raj MD & Sulochana CH, “An efficient lung segmentation ap-proach for interstitial lung disease”, Proceedings of the Interna-tional Conference on Circuit, Power and Computing Technolo-gies (ICCPCT), pp. 1211-1216, 2014.
    15. Ebrahimian H, Noor NM, Rijal OM, Yunus A & Kassim RM, “Gabor texture measure in interstitial lung disease discrimination using high resolution computed tomography thorax images”, Proceedings of the IEEE Conference on Biomedical Engineering and Sciences (IECBES), pp. 827-831, 2014.
    16. Ming JTC, Noor NM, Rijal OM, Kassim RM and Yunus A, “En-hanced automatic lung segmentation using graph cut for Intersti-tial Lung Disease”, Proceedings of the IEEE Conference on Bio-medical Engineering and Sciences (IECBES), pp. 17-21, 2014.
    17. Vo KT and Sowmya A, “Scale-space representation of lung HRCT images for diffuse lung disease classification. Proceedings of the International Conference on Image and Signal Processing, pp. 550-558, 2010.
    18. Yokota K, Maeda S, Kim H, Tan JK, Ishikawa S, Tachibana R, Hirano Y & Kido S, “Automatic detection of GGO regions on CT images in LIDC dataset based on statistical features. In Soft Computing and Intelligent Systems (SCIS)”, Proceedings of the Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), pp. 1374-1377, 2014.
    19. Shouno H, Suzuki S & Kido S, “A transfer learning method with deep convolutional neural network for diffuse lung disease clas-sification”, Proceedings of the International Conference on Neu-ral Information Processing, pp. 199-207, 2015.
    20. Ramos J, Kockelkorn TT, Ramos I, Ramos R, Grutters J, Viergever MA, van Ginneken B and Campilho A, “Content-based image retrieval by metric learning from radiology reports: Application to interstitial lung diseases”, IEEE journal of biomed-ical and health informatics, Vol. 20, No. 1, pp.281-292, 2016.
    21. Anthimopoulos M, Christodoulidis, S, Christe A & Mougiakakou S, “Classification of interstitial lung disease patterns using local DCT features and random forest”, Proceedings of the 36th Annu-al International Conference on Engineering in Medicine and Bi-ology Society (EMBC), pp. 6040-6043, 2014.
    22. Noor NM, Rosid R., Azmi MH, Rijal OM, Kassim RM & Yunus A, “Comparing watershed and FCM segmentation in detecting reticular pattern for interstitial lung disease”, Proceedings of the IEEE EMBS Conference on Biomedical Engineering and Scienc-es (IECBES), pp. 944-949, 2012.
    23. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A and Mougiakakou S, “Lung pattern classification for interstitial lung diseases using a deep convolutional neural network”, IEEE transactions on medical imaging, 35(5), pp.1207-1216, 2016.
    24. Wei Y, Xia W, Lin M, Huang J, Ni B, Dong J, Zhao Y & Yan S, “Hcp: A flexible cnn framework for multi-label image classifica-tion”, IEEE transactions on pattern analysis and machine intelli-gence, Vol.38, No. 9, pp.1901-1907, 2016.
    25. Gao M, Bagci U, Lu L, Wu A, Buty M, Shin HC, Roth H, Papa-dakis GZ, Depeursinge A, Summers RM & Xu Z, “Holistic clas-sification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks”, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualiza-tion, pp.1-6, 2016.
    26. Krizhevsky A, Sutskever I & Hinton GE, “Imagenet classifica-tion with deep convolutional neural networks”, Proceedings of the Advances in neural information processing systems, pp. 1097-1105, 2012.
    27. Kaur S, Hooda R, Mittal A & Sofat S, “Deep CNN-Based Method for Segmenting Lung Fields in Digital Chest Radio-graphs”, Proceedings of the Advanced Informatics for Computing Research, pp. 185-194, 2017.
    28. Wang Q, Zheng Y, Yang G, Jin W, Chen X & Yin Y, “Mul-tiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification”, IEEE journal of biomedical and health informatics, Vol. 22, No. 1, pp.184-195, 2018.
    29. Christodoulidis S, Anthimopoulos M, Ebner L, Christe A & Mougiakakou S, “Multisource transfer learning with convolution-al neural networks for lung pattern analysis”, IEEE journal of bi-omedical and health informatics, Vol. 21, No. 1, pp.76-84, 2017.
    30. Kim GB, Jung KH, Lee Y, Kim HJ, Kim N, Jun S, Seo JB & Lynch DA, “Comparison of Shallow and Deep Learning Meth-ods on Classifying the Regional Pattern of Diffuse Lung Dis-ease”, Journal of digital imaging, pp.1-10, 2017.
    31. Ajin M & Mredhula L, “Diagnosis of Interstitial Lung Disease by Pattern Classification”, Procedia Computer Science, Vol. 115, pp.195-208, 2017.
    32. Joyseeree R, Müller H & Depeursinge A, “Rotation-Covariant Tissue Analysis for Interstitial Lung Diseases Using Learned Steerable Filters: Performance Evaluation and Relevance for Di-agnostic Aid”, Computerized Medical Imaging and Graphics, 2018.
    33. Nurmi HM, Kettunen HP, Suoranta SK, Purokivi MK, Kärk-käinen MS, Selander TA & Kaarteenaho RL, “Several high-resolution computed tomography findings associate with survival and clinical features in rheumatoid arthritis-associated interstitial lung disease”, Respiratory Medicine, Vol. 134, pp.24-30, 2018.
    34. Lim J, Kim N, Seo JB, Lee YK, Lee Y & Kang SH, “Regional context-sensitive support vector machine classifier to improve au-tomated identification of regional patterns of diffuse interstitial lung disease”, Journal of digital imaging, Vol. 24, No. 6, pp. 1133-1140, 2011.
    35. Jun S, Kim N, Seo JB, Lee YK & Lynch DA, “An Ensemble Method for Classifying Regional Disease Patterns of Diffuse In-terstitial Lung Disease Using HRCT Images from Different Vendors”, Journal of digital imaging, Vol. 30, No. 6, pp. 761-771, 2017.
    36. Jun S, Park B, Seo JB, Lee S & Kim N, “Development of a Computer-Aided Differential Diagnosis System to Distinguish Between Usual Interstitial Pneumonia and Non-specific Intersti-tial Pneumonia Using Texture-and Shape-Based Hierarchical Classifiers on HRCT Images”, Journal of digital imaging, pp.1-10, 2017.
    37. O’Neil A, Shepherd M, Beveridge E & Goatman K, “A Compar-ison of Texture Features Versus Deep Learning for Image Classi-fication in Interstitial Lung Disease”, Proceedings of the Annual Conference on Medical Image Understanding and Analysis, pp. 743-753, 2017.
    38. Depeursinge A, Vargas A, Gaillard F, Platon A, Geissbuhler A, Poletti PA & Müller H, “Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows”, In-ternational journal of computer assisted radiology and surgery, Vol. 7, No. 1, pp.97-110, 2012.
    39. Moon JW, Bae JP, Lee HY, Kim N, Chung MP, Park HY, Chang Y, Seo JB & Lee KS, “Perfusion-and pattern-based quantitative CT indexes using contrast-enhanced dual-energy computed to-mography in diffuse interstitial lung disease: relationships with physiologic impairment and prediction of prognosis”, European radiology, Vol. 26, No. 5, pp.1368-1377, 2016.
    40. Depeursinge A, Iavindrasana J, Hidki A, Cohen G, Geissbuhler A, Platon A, Poletti PA & Müller H, “Comparative performance analysis of state-of-the-art classification algorithms applied to lung tissue categorization”, Journal of digital imaging, Vol. 23, No. 1, pp.18-30, 2010.

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  • How to Cite

    Ummay Atiya, S., & V.K Ramesh, N. (2018). Pattern classification of interstitial lung disease in high resolution clinical datasets: A systematic review. International Journal of Engineering and Technology, 7(2.7), 114-119. https://doi.org/10.14419/ijet.v7i2.7.10275