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Detection of Renal Calculi in Ultrasound Image Using Meta-Heuristic Support Vector Machine

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Abstract

Typically, the acquired renal ultrasound image includes a course of speckle noises. This paper primarily investigates an approach for the detection of renal calculi by processing those raw US images with the help of a meta-heuristic SVM classifier. One of the major downsides of involving Ultrasound images in medical analysis is the prevalence of Speckle Noises. An Adaptive Mean Median Filter approach has been introduced in the work to get rid of the speckle noises to the maximum extent ever in the literature. Segmentation is performed by employing conventional K-Means and GLCM features were extracted for classification using a meta-heuristic SVM classifier. The proposed methodology investigates with a Real-time Acquired Dataset of Mithra Scans, Tamilnadu, India comprises of 250 clinical Ultra-Sound Kidney Images of which 150 are having Calculi and the rest are Healthy. With the experimental results, the proposed meta-heuristic SVM classifier have performed better in noisy images while comparing with other conventional methods considered in the literature. It exhibits an Accuracy of 98.8% with a FAR rate of 1.8 for FRR as high as 3.3. The results clearly proposed that the novel AMM-PSO-SVM could be a promising technique for object or foreign body detection in a medical imaging application that uses ultrasound imaging.

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References

  1. Tamilselvi, P. R., Region Indicator with Contour Segmentation (RICS) method. Snowbird: Proc. IEEE Data Compression Conf, 1998, 408–417.

    Google Scholar 

  2. Akkasaligar, P. T., and Biradar, S., Diagnosis of Renal Calculus Disease in Medical Ultrasound Images. IEEE International Conference on Computational Intelligence and Computing Research, 2016 978-1-5090-0612-0/16.

  3. El-Naqa, I., Yang, Y., Wernick, N., Galatsanos, N. P., and Nishikawa, R. M., Support Vector Machine approach for detection of Micro calcifications. IEEE Trans. Med. Imaging 21(12):1552–1563, 2002.

    Article  PubMed  Google Scholar 

  4. Ebrahimi, S., and Mariano, V. Y., Image Quality Improvement in Kidney Stone Detection on Computed Tomography Images. Journal of Image and Graphics 3(1):40–46, 2015.

    Article  Google Scholar 

  5. Aadhirai, S., and Najumnissa Jamal, D., Feature Extraction and Analysis of Renal Abnormalities using Fuzzy Clustering Segmentation and SIFT Method. International Conference on Biosignals, Images and Instrumentation (ICBSII), 978-1-5090-4980-6, 2017.

  6. Jeyalakshmi, T. R., Linear filtering techniques like spatial averaging have blurring effect and Adaptive filtering techniques. LNCS 3216:549–557. Springer, Heidelberg, 2004.

    Google Scholar 

  7. Kim, D.-Y., and Park, J.-W., Computer-Aided Detection of Kidney Tumor on Abdominal Computed Tomography Scans. Acta Radiol. 45(7):791–795, 2004.

    Article  CAS  PubMed  Google Scholar 

  8. José Antonio Martín, H., Montero, J., Yáñez, J., and Gómez, D., A Divisive Hierarchical k-means based Algorithm for Image Segmentation. IEEE International Conference on Intelligent Systems and Knowledge Engineering, China, 978-1-4244-6793-8, 2011.

  9. Jiang, X. J., kidney segmentation of ultrasound (US) images combined with nonlocal total variation (NLTV) image de-noising, distance regularized level set evolution (DRLSE) and shape prior. IJCSI International Journal of Computer Science Issues 7(5):414–417, 2010.

    Google Scholar 

  10. Srivastava, D. K., and Bhambhu, L., Data Classification using support vector Machines. J. Theor. Appl. Inf. Technol., 2009.

  11. Wei, J., and Jian-Q i, Z., Xiang, Z. Face recognition method based on support vector machine and particle swarm optimization. Expert Syst. Appl. 38(4):4390–4393, 2011.

    Article  Google Scholar 

  12. Dhrumil, S., and Shah, S., Ultrasound Image Segmentation Techniques for Renal Calculi - A Review. European Journal of Academic Essays 1(10):51–55, 2014.

    Google Scholar 

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Correspondence to S. Selvarani.

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Selvarani S declares that she has no conflict of interest. Rajendran P declares that she has no conflict of interest.

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Selvarani, S., Rajendran, P. Detection of Renal Calculi in Ultrasound Image Using Meta-Heuristic Support Vector Machine. J Med Syst 43, 300 (2019). https://doi.org/10.1007/s10916-019-1407-1

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  • DOI: https://doi.org/10.1007/s10916-019-1407-1

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