Acta Mechanica Slovaca 2019, 23(4):38-45 | DOI: 10.21496/ams.2020.014

Modeling of Kidney Stones from Ultrasound Images based on Hybrid Regional Segmentation with Active Contours

Dominik Vilimek, Jan Kubicek, Adela Kloudova, Alice Krestanova, Marek Penhaker, Martin Cerny, Martin Augustynek, David Oczka, Daniel Barvik
VSB-Technical University of Ostrava, FEECS, K450, 17. Listopadu 15, Ostrava-Poruba, Czech Republic

Kidney stones (nephrolithiasis) are among the most common kidney diseases. They are solid stones that arise from minerals dissolved in urine. The treatment of kidney stones depends primarily on the position, size and composition of the stones, as well as on the general health condition of the patient. However, early diagnosis is quite complicated. In this paper, we propose a fully automatic hybrid method for identification and features extraction of the kidney stones. Specifically, model is based on the multiregional segmentation and approximation of the kidney stone area. Methods consequently use concept of the active contours which are focused on extraction of the geometrical features. The method remarkably allowing for an objective monitoring and classification of the kidney stones. These results may play a role to overcome conventionally used clinical procedures where clinicians mark kidney stones manually, without software feedback.

Keywords: segmentation; kidney stones; ultrasound; active contours; regional segmentation

Published: December 20, 2019  Show citation

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Vilimek, D., Kubicek, J., Kloudova, A., Krestanova, A., Penhaker, M., Cerny, M., ... Barvik, D. (2019). Modeling of Kidney Stones from Ultrasound Images based on Hybrid Regional Segmentation with Active Contours. Acta Mechanica Slovaca, 23(4), 38-45. doi: 10.21496/ams.2020.014
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    References

    1. Geraghty, R.M., Jones, P., Somani, B.K.: Worldwide Trends of Urinary Stone Disease Treatment Over the Last Two Decades: A Systematic Review. Journal of Endourology 31(6), 547-556 (Jun 2017) Go to original source...
    2. Khan, S.R., Pearle, M.S., Robertson, W.G., Gambaro, G., Canales, B.K., Doizi, S., Traxer, O., Tiselius, H.G.: Kidney stones. Nature Reviews Disease Primers 2(1), 16008 (Dec 2016) Go to original source...
    3. Rocca Rossetti, S.: Kidney Stones. In: Managing Segmental Renal Diseases, pp. 15{18. Springer International Publishing, Cham (2018) Go to original source...
    4. Roberson, N.P., Dillman, J.R., O'Hara, S.M., DeFoor, W.R., Reddy, P.P., Giordano, R.M., Trout, A.T.: Comparison of ultrasoundversus computed tomography for the detection of kidney stones in the pediatric population: A clinical efectiveness study. Pediatric Radiology 48(7), 962{972 (Jul 2018) Go to original source...
    5. Viswanath, K., Gunasundari, R., Hussain, S.A.: Analysis of Kidney stone Detection by Reaction diffusion Level Set Segmentation and Xilinx System Generator. In: Proceedings of the 2015 International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET 2015) - ICARCSET '15. pp. 1{9. ACM Press, Unnao, India (2015) Go to original source...
    6. Akkasaligar, P.T., Biradar, S.: Diagnosis of renal calculus disease in medical ultrasound images. In: 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). pp. 1{5. IEEE, Chennai (Dec 2016) Go to original source...
    7. Goel, N., Yadav, A., Singh, B.M.: Medical image processing: A review. In: 2016 Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with Their Impact on Humanity (CIPECH). pp. 57{62. IEEE, Ghaziabad, India (Nov 2016) Go to original source...
    8. Viswanath, K., Gunasundari, R.: Design and analysis performance of kidney stone detection from ultrasound image by level set segmentation and ANN classi_cation. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp. 407{414. IEEE, Delhi, India (Sep 2014) Go to original source...
    9. Akkasaligar, P.T., Biradar, S., Badiger, S.: Segmentation of Kidney Stones in Medical Ultrasound Images. In: Santosh, K.C., Hegadi, R.S. (eds.) Recent Trends in Image Processing and Pattern Recognition, vol. 1036, pp. 200{208. Springer Singapore, Singapore (2019) Go to original source...
    10. Vijayakumar, M., Ganpule, A., Singh, A., Sabnis, R., Desai, M.: Review of techniques for ultrasonic determination of kidney stone size. Research and Reports inUrology Volume 10, 57{61 (Aug 2018) Go to original source...
    11. Marsousi, M., Plataniotis, K.N., Stergiopoulos, S.: Shape-based kidney detection and segmentation in three-dimensional abdominal ultrasound images. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 2890{2894}. IEEE, Chicago, IL (Aug 2014) Go to original source...
    12. Akkasaligar, P.T., Biradar, S., Kumbar, V.: Kidney stone detection in computed tomography images. In: 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon). pp. 353{356. IEEE, Bangalore (Aug 2017) Go to original source...
    13. Guanyu Yang, Jinjin Gu, Yang Chen, Wangyan Liu, Lijun Tang, Huazhong Shu, Toumoulin, C.: Automatic kidney segmentation in CT images based on multi-atlas image registration. In: 2014 36th Annual International Conferenceof the IEEE Engineering Go to original source...
    14. Mittal, A., Moorthy, A.K., Bovik, A.C.: No-Reference Image Quality Assessment in the Spatial Domain. IEEE Transactions on Image Processing 21(12), 4695{4708}. (Dec 2012) Go to original source...
    15. Outtas, M., Zhang, L., Deforges, O., Hamidouche, W., Serir, A.: Evaluation of No-reference quality metrics for Ultrasound liver images. In: 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX). pp. 1{3. IEEE}, Cagliari (May 2018) Go to original source...
    16. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a \\Completely Blind" Image Quality Analyzer. IEEE Signal Processing Letters 20(3), 209{212 (Mar 2013) Go to original source...

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