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Ultra Sound Imaging System of Kidney Stones Using Deep Neural Network

  • S. R. BalajiEmail author
  • R. Manikandan
  • S. Karthikeyan
  • R. Sakthivel
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)

Abstract

Magnetic resonance is one of the imaging modality through which medical images can be diagnosed. Each modality like imaging (MRI), Ultrasonography (US), Intravenous Urography (IVU), computed Tomography (CT), Angiography (AG) has their own advantages and disadvantages in various aspects like formation, sensitivity, resolution, level of invasive and cost. Both MRI and CT scan give same information in regarding to kidney imaging. However in MRI, the material gadolinium is associated with Nephroenic Systemic Fibrosis (NSF), which decreases the kidney functioning. The measurement of size and shape of the kidney and evaluation of pelvis and ureters is done by IVU. The major drawback is that, there may be renal failure due to radiation and 1 V contrast administration. In CT the tomographic image is formed by computer processed image, where more information is same as ultrasound. The major advantages are excellent spatial/contrast resolution and low cost. However there is a major drawback of exposure of radiation and contrast dye causes damage to kidney. In Radiography, the kidney stones are distinguished by the ratio of absorption over dark field signal. The limitation of radiography is that it can determine only the intensities of average signal through the projection of x-ray direction. In this project enhancement (Smoothening and Sharpening) technique is used in order to contrast the image. We use the active contour segmentation in order to extract the particular portion of image and then deep neural network is used in order to classify the various types of stones.

Keywords

Ultra Sound image Deep Neural Network Feature extraction Region of Interest 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • S. R. Balaji
    • 1
    Email author
  • R. Manikandan
    • 1
  • S. Karthikeyan
    • 2
  • R. Sakthivel
    • 1
  1. 1.Department of EIEPanimalar Engineering CollegeChennaiIndia
  2. 2.Department of ECESathyabama Institute of Science and TechnologyChennaiIndia

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