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Automated detection of chronic kidney disease using higher-order features and elongated quinary patterns from B-mode ultrasound images

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Abstract

Chronic kidney disease (CKD) is a continuing loss of kidney function, and early detection of this disease is fundamental to halting its progression to end-stage disease. Numerous methods have been proposed to detect CKD, mainly focusing on classification based upon peripheral clinical parameters and quantitative ultrasound parameters that must be manually calculated, or on shear wave elastography. No studies have been found that detect the presence or absence of CKD based solely from one B-mode ultrasound image. In this work, we propose an automated system to detect chronic kidney disease utilizing only the automatic extraction of features from a B-mode ultrasound image of the kidney, with a database of 405 images. Higher-order bispectrum and cumulants, and elongated quinary patterns, are extracted from each image to provide a final total of 24,480 features per image. These features were subjected to a locality sensitive discriminant analysis (LSDA) technique, which provides 30 LSDA coefficients. The coefficients were arranged according to their t value and inserted into various classifiers, to yield the best diagnostic accuracy using the least number of features. The best performance was obtained using a support vector machine and a radial basis function, utilizing only five features, resulting in an accuracy of 99.75%, a sensitivity of 100%, and a specificity of 99.57%. Based upon these findings, it is evident that the technique accurately and automatically identifies subjects with and without CKD from B-mode ultrasound images.

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Acknowledgements

The study was supported by UMSE CA.R.E fund (PV018-2018), University of Malaya. We expressed our gratitude to Dr Maisarah Jalalonmuhali for helping in this study.

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore

    U. Rajendra Acharya, Joel En Wei Koh, Yuki Hagiwara & Shu Lih Oh

  2. Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore

    U. Rajendra Acharya

  3. School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Subang Jaya, Malaysia

    U. Rajendra Acharya

  4. Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy

    Kristen M. Meiburger & Filippo Molinari

  5. Department of Biomedical Imaging, University of Malaya, Kuala Lumpur, Malaysia

    Sook Sam Leong, Jeannie Hsiu Ding Wong, Mohammad Nazri Md Shah & Kwan Hoong Ng

  6. Department of Medicine, Columbia University, New York, USA

    Edward J. Ciaccio

  7. Department of Biomedical Imaging, University of Malaya Medical Centre, Kuala Lumpur, Malaysia

    Sook Sam Leong

  8. University of Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia

    Jeannie Hsiu Ding Wong, Mohammad Nazri Md Shah & Kwan Hoong Ng

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  1. U. Rajendra Acharya
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Correspondence to U. Rajendra Acharya.

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Acharya, U.R., Meiburger, K.M., Koh, J.E.W. et al. Automated detection of chronic kidney disease using higher-order features and elongated quinary patterns from B-mode ultrasound images. Neural Comput & Applic 32, 11163–11172 (2020). https://doi.org/10.1007/s00521-019-04025-y

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