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Paper Details
Paper Title
Design and Analysis Performance of Kidney Cyst Detection from Ultrasound Images
Authors
  M. P. Pawar,  A. N. Mulla
Abstract
The research presents a system which is mainly pointing to the analysis of kidney and its abnormality as a cyst. The main goal of research is classification of ultrasound (US) kidney image as normal kidney or cystic one. The system with trained template is developed and user’s sample tests are verified from it. Ultrasound images contain a noise called speckle noise. It is multiplicative noise and it is introduced due to signal modification at the time of capturing an image. US images also suffers by low contrast. These issues are sorted out using filter technique and histogram equalization method. The pre-processed image is segmented using Gradient Vector Flow (GVF) and from it region of interest is identified. 22 features of an image are extracted and these features are trained by feed forward Artificial Neural Network (ANN) to identify the class of kidney (i.e. normal or cyst). In order to analyse the systems functionality, it is tested on a dataset of ultrasound images of two classes. The analysis performance is based on two parameters first is accuracy and second is precision, which results in 87.5% and 100% respectively.
Keywords- US kidney images, Speckle noise, GVF, ANN.
Publication Details
Unique Identification Number - IJEDR1704149Page Number(s) - 911-917Pubished in - Volume 5 | Issue 4 | November 2017DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  M. P. Pawar,  A. N. Mulla,   "Design and Analysis Performance of Kidney Cyst Detection from Ultrasound Images", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 4, pp.911-917, November 2017, Available at :http://www.ijedr.org/papers/IJEDR1704149.pdf
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