Enhanced Kidney Stone Identification Using Ultrasonographic Images in Image Processing


  • Pil-Kee Min Department of Informatics, The University of Electro-Communications (UEC), Tokyo, 182-8585, Japan
  • Debnath Bhattacharyya Computer Science and Engineering Department, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India
  • Byung Chan Min Hanbat National University, Department of Industrial & Management Engineering, 34158, Daejeon, Republic of Korea
  • Tae-Hoon Kim School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China
  • Kazuyuki Mito Department of Informatics, The University of Electro-Communications (UEC), Tokyo, 182-8585, Japan


kidney stones, computed tomography, image processing, Ultrasonographic Images


Stones, cysts, urinary tract obstruction, birth defects, and malignant cells are just some of the problems that may manifest in the kidneys. Kidney stone disease occurs when a stone is formed in the kidney or elsewhere in the urinary system. There may be no ill effects from passing the little stone. Pain in the lower back or abdomen may be experienced if a stone develops to a size greater than 5 millimetres and blocks the ureter. Thus, a method of detecting kidney stones is required to prevent future medical complications. The primary goal of this work is to use different image processing algorithms to identify the kidney stone in a digital ultrasound picture of the kidney. Yet, owing to poor contrast and the presence of speckle noise, the ultrasound-generated picture is unfit for further processing. Although improving the ultrasound picture quality by denoising methods was also a focus of the research, this was also investigated. More so, the improved ultrasound picture is utilised to pinpoint the precise location of the stone. The primary objective of this work was to provide a simple and efficient method for locating kidney stone. As this can be done on any computer, any healthy person may potentially check for a kidney stone using ultrasound and begin the process of dissolving it right away. Depending on the size and position of the lesion, these methods greatly aid the doctor in proceeding with further treatment.


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Janarthanan, S., Ashok, A. Guruprasad, S. S.,, Mounesa, P. Madhan Mohan, M. and Baluprithviraj, K. N., "Investigation of Kidney Stone Detection using Image Processing," 2022 International Conference on Edge Computing and Applications (ICECAA), Tamilnadu, India, 2022, pp. 946-951, doi: 10.1109/ICECAA55415.2022.9936089.

M. Akshaya, R. Nithushaa, N. S. M. Raja and S. Padmapriya, "Kidney Stone Detection Using Neural Networks," 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2020, pp. 1-4, doi: 10.1109/ICSCAN49426.2020.9262335..

Soni and A. Rai, "Kidney Stone Recognition and Extraction using Directional Emboss & SVM from Computed Tomography Images," 2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT), Shivamogga, India, 2020, pp. 57-62, doi: 10.1109/MPCIT51588.2020.9350388.

S. Rajput, A. Singh and R. Gupta, "Automated Kidney Stone Detection Using Image Processing Techniques," 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2021, pp. 1-5, doi: 10.1109/ICRITO51393.2021.9596175..

M. B, N. Mohan, S. K. S and S. K. P, "Automated Detection of Kidney Stone Using Deep Learning Models," 2022 2nd International Conference on Intelligent Technologies (CONIT), Hubli, India, 2022, pp. 1-5, doi: 10.1109/CONIT55038.2022.9847894.

J. Jendeberg, P. Thunberg, and M. Lidén, “Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network,” Urolithiasis, Feb. 2020, doi: https://doi.org/10.1007/s00240-020-01180-z.

R. Anand, V. Sowmya, Vijaykrishnamenon, E. A. Gopalakrishnan, And K. P. Soman, “Modified Vgg Deep Learning Architecture For Covid-19 Classification Using Bio-Medical Images,” IOP Conference Series: Materials Science and Engineering, vol. 1084, no. 1, p. 012001, Mar. 2021, doi: https://doi.org/10.1088/1757-899x/1084/1/012001.

Aksakalli, S. Kaçdioğlu, and Y. S. Hanay, “Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods,” Balkan Journal of Electrical and Computer Engineering, pp. 144–151, Apr. 2021, doi: https://doi.org/10.17694/bajece.878116.

Verma, M. Nath, P. Tripathi, and K. K. Saini, “Analysis and identification of kidney stone using Kth nearest neighbour (KNN) and support vector machine (SVM) classification techniques,” Pattern Recognition and Image Analysis, vol. 27, no. 3, pp. 574–580, Jul. 2017, doi: https://doi.org/10.1134/s1054661817030294.

M. Alwan And S. Sadek, “Investigation of Kidney Stone Using a Microstrip Patch Antenna Scanning System,” Journal of Diagnostics, vol. 3, no. 1, pp. 1–10, 2016, doi: https://doi.org/10.18488/journal.98/2016.3.1/

R. Islam, F. Mahbub, S. Abdul Kadir Al-Nahiun, S. Banerjee Akash, R. Rashidul Hasan and A. Rahman, "Design of an On-Body Rectangular Microstrip Patch Antenna for the Diagnosis of Breast Cancer Using S-Band", Proceedings of Sixth International Congress on Information and Communication Technology, pp. 1033-1044, 2022

Stoecker, W. V., Zhang, Z., Moss, R. H., Umbaugh, S. E., & Ercal, F. (1997). Boundary detection techniques in medical image processing. General Anatomy, 4, 1.

Hafizah, W. M., Supriyanto, E., & Yunus, J. (2012, May). Feature extraction of kidney ultrasound images based on intensity histogram and Gray level co-occurrence matrix. In 2012 Sixth Asia Modelling Symposium (pp. 115-120). IEEE.

HusseinAli, A., Hasan, E. H., & Naeemah, M. R. Kidney Texture Classification Using Local Binary Pattern and Geometrical Features.

Qadri, S. (2021). Role of Machine Vision for Identification of Kidney Stones Using Multi Features Analysis. Lahore Garrison University Research Journal of Computer Science and Information Technology, 5(3), 1-14.

Alkurdy, N. H., Aljobouri, H. K., & Wadi, Z. K. (2023). Ultrasound renal stone diagnosis based on convolutional neural network and vgg16 features. Int J Electr Comput Eng, 13(3), 3440-3448.

Vishmitha, D., Yoshika, K., Sivalakshmi, P., Chowdary, V., Shanthi, K. G., & Yamini, M. (2022, September). Kidney Stone Detection Using Deep Learning and Transfer Learning. In 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 987-992). IEEE.

Ravanappan, P. ., Ilanchezhian, P. ., Chandrasekaran, N. ., Prabu, S. ., & Saranya, N. N. . (2023). Secure Blockchain Transactions for Electronic Health Records based on an Improved Attribute-Based Signature Scheme (IASS). International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 77–83. https://doi.org/10.17762/ijritcc.v11i4s.6309

Prof. Virendra Umale. (2020). Design and Analysis of Low Power Dual Edge Triggered Mechanism Flip-Flop Employing Power Gating Methodology. International Journal of New Practices in Management and Engineering, 6(01), 26 - 31. https://doi.org/10.17762/ijnpme.v6i01.53

Sharma, R., Dhabliya, D. A review of automatic irrigation system through IoT (2019) International Journal of Control and Automation, 12 (6 Special Issue), pp. 24-2




How to Cite

Min, P.-K. ., Bhattacharyya, D. ., Min, B. C. ., Kim, T.-H. ., & Mito, K. . (2023). Enhanced Kidney Stone Identification Using Ultrasonographic Images in Image Processing. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 477–484. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3809



Research Article