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Detection of Liver Tumor Using Gradient Vector Flow Algorithm

  • Jisha BabyEmail author
  • T. Rajalakshmi
  • U. Snekhalatha
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Liver tumor also known as the hepatic tumor is a type of growth found in or on the liver. Identifying the tumor location can be a tedious, error-prone and need an experts study to identify it. This paper presents a segmentation technique to segment the liver tumor using Gradient Vector Flow (GVF) snakes algorithm. To initiate snakes algorithm the images need to be insensitive to noise, Wiener Filter is proposed to remove the noise. The GVF snake starts its process by initially extending it to create an initial boundary. The GVF forces are calculated and help in driving the algorithm to stretch and bend the initial contour towards the region of interest due to the difference in intensity. The images were classified into tumor and non-tumor categories by Artificial Neural Network Classifier depending on the features extracted which showed notable dissimilarity between normal and abnormal images.

Keywords

Gradient vector flow (GVF) Wiener filter Edge mapping Artificial neural networks (ANNs) 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringSRM Institute of Science and TechnologyChennaiIndia

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