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

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...

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Published in:Journal of Medical Systems Vol. 43; no. 9
Main Authors: Selvarani, S., Rajendran, P.
Format: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature Sep2019
Online Access:View this record in EBSCOhost
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      jtl: Journal of Medical Systems
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      dt: Sep2019
      vid: 43
      iid: 9
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-019-1407-1
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        atl: Detection of Renal Calculi in Ultrasound Image Using Meta-Heuristic Support Vector Machine.
      aug:
        au:
          Selvarani, S.
          Rajendran, P.
        affil: Department of ECE, Muthayammal College of Engineering, Rasipuram, India
      sug:
        subj:
          Kidney Calculi Ultrasonography
          Image Interpretation, Computer Assisted
          Noise Prevention and Control
          Human
          India
          Foreign Bodies Diagnosis
          Image Processing, Computer Assisted
          Algorithms
      ab: 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.
      pubtype: Academic Journal
      doctype:
        algorithm
        diagnostic images
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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