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SVM-Based CAC System for B-Mode Kidney Ultrasound Images

Journal of Digital Imaging volume 28, pages 448–458 (2015)Cite this article

Abstract

The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee’s sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.

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Acknowledgments

The authors would like to thank the reviewers for the valuable review, which led to the significant improvement in the manuscript. Author Subramanya M. B. is grateful to Dr. Jitendra Virmani for the timely suggestions and the Ministry of Human Resource and Development (MHRD), India, for the scholarship. The authors would like to acknowledge the Department of Electrical Engineering, Indian Institute of Technology, Roorkee, and Department of Radiology, Himalayan Institute of Hospital and Trust, for their support during the period of this research work.

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Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    M. B. Subramanya & Vinod Kumar

  2. Moradabad Institute of Technology, Moradabad, Uttar Pradesh, 244001, India

    Shaktidev Mukherjee

  3. Department of Radiology, Himalayan Institute of Medical Sciences, HIHT University, PO Doiwala, Dehradun, 248140, India

    Manju Saini

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  1. M. B. Subramanya
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  2. Vinod Kumar
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  3. Shaktidev Mukherjee
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  4. Manju Saini
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Correspondence to M. B. Subramanya.

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Subramanya, M.B., Kumar, V., Mukherjee, S. et al. SVM-Based CAC System for B-Mode Kidney Ultrasound Images. J Digit Imaging 28, 448–458 (2015). https://doi.org/10.1007/s10278-014-9754-4

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  • DOI: https://doi.org/10.1007/s10278-014-9754-4

Keywords

  • Ultrasound kidney images
  • Texture features
  • Feature selection
  • Support vector machine
  • Classification