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Segmentation of Kidney Stones in Medical Ultrasound Images

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Book cover Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1036))

Abstract

The computer-aided diagnostic system has become an important issue in clinical diagnosis. Development of new technologies and use of various imaging modalities have raised more challenging issues. The major issue is processing and analyzing a significantly large volume of image data, to generate qualitative information for diagnosis and treatment of diseases. Medical imaging, particularly ultrasound imaging is one of the commonly used diagnostic tool by medical experts. Segmenting a region of interest in medical ultrasound image is a difficult task because of variation in object shape, orientation and image quality. In the present study, initially preprocessing of kidney ultrasound images is performed using contourlet transform and contrast enhancement using histogram equalization. The proposed method focuses on segmentation of kidney stones in preprocessed medical ultrasound images using level set method. The developed method shows better performance in segmenting renal calculi in medical ultrasound images of the kidney. The experimental results demonstrate the effectiveness of the developed software module.

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Acknowledgement

The authors are thankful to Vision Group of Science and Technology (VGST), government of Karnataka for financial support under RGS/F scheme. The authors are also thankful to Dr. Bhushita B. Lakhkar, Assistant Professor, Department of Radiology, BLDEDU’s Sri. B. M. Patil Medical College and Research Centre, Vijayapur for assisting us in getting kidney USG images for preparing clinical data set for experimentation. She has also provided expert opinion for framing the ground truth. Authors also would like to thank Dr. Vinay Kundaragi, Nephrologist, Sri. B. M. Patil Medical College and Research Centre, Vijayapur for manual segmentation of USG images.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, BLDEA’s V.P. Dr. P.G. H. College of Engineering and Technology, Vijayapur, Karnataka, India

    Prema T. Akkasaligar & Sunanda Biradar

  2. Department of Medicine, BLDEDU’s Sri. B. M. Patil Medical College and Research Centre, Vijayapur, Karnataka, India

    Sharan Badiger

Authors
  1. Prema T. Akkasaligar
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  2. Sunanda Biradar
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  3. Sharan Badiger
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Corresponding author

Correspondence to Sunanda Biradar .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Akkasaligar, P.T., Biradar, S., Badiger, S. (2019). Segmentation of Kidney Stones in Medical Ultrasound Images. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_18

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  • DOI: https://doi.org/10.1007/978-981-13-9184-2_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9183-5

  • Online ISBN: 978-981-13-9184-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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