Segmentation of Liver from Abdomen CT Images Using Classification and Regression Tree

Authors

  • Dayana TRN Dept. of CSE, Bharathiar University, Coimbatore, India
  • Fred AL Dept. of CSE, Mar Ephraem College of Engineering and Technology, Marthandam, India

DOI:

https://doi.org/10.26438/ijcse/v7i1.327332

Keywords:

Decision tree, Segmentation, Classification, regression tree

Abstract

Segmentation role is inevitable in image processing for the extraction of the desired region of interest. This work proposes decision tree for the segmentation of liver from abdomen CT images. Prior to feature extraction and segmentation, feature extraction was performed by the median filter. The hybrid feature extraction comprising of GLCM and LBP is used and training phase comprises of 20 DICOM CT abdomen images. The morphological operations are performed in the post processing phase for the refinement of output. The algorithms are developed in Matlab 2010a and tested on real time abdomen CT images.

References

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Published

2019-01-31
CITATION
DOI: 10.26438/ijcse/v7i1.327332
Published: 2019-01-31

How to Cite

[1]
T. N. Dayana and A. L. Fred, “Segmentation of Liver from Abdomen CT Images Using Classification and Regression Tree”, Int. J. Comp. Sci. Eng., vol. 7, no. 1, pp. 327–332, Jan. 2019.

Issue

Vol. 7 No. 1 (2019): IJCSE January Edition

Section

Research Article

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