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A Study on Preprocessing Techniques for Ultrasound Images of Carotid Artery

  • Mounica S. 
  • Ramakrishnan S. 
  • Thamotharan B. Email author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Ultrasound imaging has been widely used in the diagnosis of atherosclerosis. To precisely diagnose the carotid plaque, the affected region should be segmented from the ultrasonic image of carotid artery. Many techniques have been used to identify the plaque in ultrasound images. Image enhancement and restoration are the important processes to acquire high-quality images from the noisy images. When the artery images are captured, noise occurs due to high-frequency rate. To acquire a high-quality image, preprocessing is the first step to be done. The quality of the image is improved in this process. The techniques involved in preprocessing are dealt in this paper. Preprocessing involves filtering the image and removing the noise by various filtering techniques. Salt-and-pepper and Gaussian noise in ultrasound images can be filtered using techniques like mean, median and Wiener filters. Salt-and-pepper noise is multiplicative in nature and it is introduced by the image acquisition mechanism. The quality of the input sensor is reflected by the Gaussian noise. In this paper, the performance of image enhancement techniques on ultrasound images are evaluated using quality metrics, namely Mean Square Error (MSE) and Peak Signal–to-Noise Ratio (PSNR).

Keywords

Ultrasound Preprocessing Filtering Quality images 

Notes

Compliance to Ethical Standards

Conflict of Interest

Author S. Mounica, Author S. Ramakrishnan and Author B. Thamotharan declares that they have no conflict of interest.

Funding

We the authors would like to thank the Department of Science and Technology, India for their financial support through Fund for Improvement of S&T Infrastructure (FIST) programme (SR/FST/ETI-349/2013).

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Acknowledgement

We the authors sincerely thank the SASTRA Deemed to be University for providing an excellent infrastructure to carry out the research work.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mounica S. 
    • 1
  • Ramakrishnan S. 
    • 1
  • Thamotharan B. 
    • 1
    Email author
  1. 1.Computer Vision & Machine Learning Laboratory, School of ComputingSASTRA Deemed UniversityThanjavurIndia

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