Stage classification in chronic kidney disease by ultrasound image

Jun-Wei Hsieh, C. Hung Lee, Y. Chih Chen, W. Shan Lee, H. Fen Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Ultrasound imaging can provide radiation-free, non-invasive, low cost, and convenient for disease detection. However, speckle effect makes it noisy and thus reduces its overall diagnostic abilities in disease analysis. This paper develops a real time system to analyze chronic kidney disease (CKD) using only Ultrasound images. As we know, this is the first work to analyze CKD stages of patients directly from ultrasound images without using any blood examination such as Creatinine index. To build the scoring index, this paper uses Nakagami distribution and Local Binary Pattern (LBP) to model the scattering properties of CKD patients' ultrasound images. In addition, we find the age distribution is also important for CKD stage analysis. After integration, a codebook concept is adopted to extract important visual codes to describe various texture and scattering characteristics of each CKD stage. Then, an ensemble scheme is proposed for CKD stage prediction and classification by separating input ultrasound images to several grids and then integrating different classifiers trained on these grids to build a strong CKD stage classifier via SVM. Experimental results demonstrate the sensitivity and specificity of this system up to 93.82% and 83.34%, respectively.

Original languageEnglish
Title of host publicationProceedings of IVCNZ 2014
Subtitle of host publicationThe 29th International Conference on Image and Vision Computing New Zealand
PublisherAssociation for Computing Machinery
Pages271-276
Number of pages6
ISBN (Electronic)9781450331845
DOIs
StatePublished - 19 Nov 2014
Event29th International Conference on Image and Vision Computing New Zealand, IVCNZ 2014 - Hamilton, New Zealand
Duration: 19 Nov 201421 Nov 2014

Publication series

NameACM International Conference Proceeding Series
Volume19-21-November-2014

Conference

Conference29th International Conference on Image and Vision Computing New Zealand, IVCNZ 2014
CountryNew Zealand
CityHamilton
Period19/11/1421/11/14

Keywords

  • Chronic kidney disease
  • Local Binary Pattern
  • Nakagami distribution
  • Support vector machine

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    Hsieh, J-W., Lee, C. H., Chen, Y. C., Lee, W. S., & Chiang, H. F. (2014). Stage classification in chronic kidney disease by ultrasound image. In Proceedings of IVCNZ 2014: The 29th International Conference on Image and Vision Computing New Zealand (pp. 271-276). (ACM International Conference Proceeding Series; Vol. 19-21-November-2014). Association for Computing Machinery. https://doi.org/10.1145/2683405.2683457