Detection and Classification of Kidney Stone in CT Images

Authors

  • Porkodi. J  Assistant Professor, Department of Biomedical Engineering, GRT Institute of Engineering and technology, Tirutani, Tamil Nadu, India
  • Thenmozhi. M  Student, Department of Biomedical Engineering, GRT Institute of Engineering and technology, Tirutani, Tamil Nadu, India
  • Priyadharshini. R  Student, Department of Biomedical Engineering, GRT Institute of Engineering and technology, Tirutani, Tamil Nadu, India

Keywords:

Bilinear Interpolation, clustering, co-occurrence, Staghorn, Calculi

Abstract

Kidney stone disease is one of the risks for life throughout the world and the majority of people with the stone formation in the kidney at the initial stage do not notice it as disease and it damages the organ slowly. The current estimation is that 30 million people are suffering from this disease. The currently available widely used imaging techniques for diagnosing kidney diseases include X-ray imaging and Ultrasound imaging. The proposed work is used to detect and classify the kidney stone using CT images. The Bilinear Interpolation and the median filter is used for image resizing and noise removal in preprocessing. Level set segmentation (Fuzzy C means clustering) is proposed for kidney stone segmentation. A novel approach for the classification of different types of Kidney stones using Random Forest and gray level co-occurrence matrix (GLCM). Different types of Kidney stones namely Cystine stones, Staghorn stones, and Renal Calculi stones were analyzed. MATLAB R2013A tool is used for this project.

References

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International Journal of Scientific Research in Science and Technology

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Published

2020-03-05

Issue

Volume 5, Issue 5, March-April-2020

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Porkodi. J, Thenmozhi. M, Priyadharshini. R, " Detection and Classification of Kidney Stone in CT Images, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 5, Issue 5, pp.184-188, March-April-2020.