Segmentation of Computed Tomography Images Using HMRF-EM Algorithm with K-Means Clustering

Authors

  • Yogesh S Bahendwar Department of Electronics and Telecommunication, SSEC, Bhilai, India
  • G R Sinha SSGI, Bhilai, India

Abstract

Disease diagnosis through medical imaging involves segmentation of acquired medical images. The medical images contain noises, artifacts, distortions due to various factors. The imaging modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Digital mammography etc. provide an effective means for noninvasively mapping the anatomy of the patient. These techniques have prominently increased the knowledge of medical researchers in normal and diseased anatomy of patients and are vital tool in diagnosis and treatment planning. MRF (Markov Random Field) model is a widely accepted tool for segmentation of medical images. In this paper, we proposed a modified HMRF algorithm and its application in segmentation of colored CT image and discussed its result.

Keywords: CT, MRI, X-ray, MRF, SPECT

Cite this Article

Yogesh S. Bahendwar, G. R. Sinha. Segmentation of computed tomography images using HMRF-EM algorithm with K-Means clustering. Research and Reviews: Journal of Computational Biology. 2015; 4(3): 14–17p.


 

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Published

2015-12-29

Issue

Vol. 4 No. 3 (2015)

Section

Research/Review Articles

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