Improving the MRI Tumor Segmentation Process Using Appropriate Image Processing Techniques

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Ahmed Basil Al-Othman 1,* Zohair Al-Ameen 1,2 Ghazali Bin Sulong 1

1. Faculty of Computing, Universiti Teknologi Malaysia

2. Faculty of Information Sciences and Engineering, Management and Science University

* Corresponding author.


Received: 8 Oct. 2013 / Revised: 7 Nov. 2013 / Accepted: 11 Dec. 2013 / Published: 8 Jan. 2014

Index Terms

Medical image processing, Tumor segmentation, MRI images, Non-local Means, Histogram equalization, Laplacian filter


Segmenting tumor from MRI images is an essential but time consuming manual duty. Performing an automatic segmentation is a defying task since different forms of tumor tissue exist for diverse patients and in many cases the tumor is similar to the normal tissue. Various studies proposed earlier to handle the issue of precisely segmenting the tumor but they discard the degradations and their effect to the precision of the segmentation. This article provides a more precise segmentation process through the use of appropriate pre-processing algorithms. The authors studied many enhancement and restoration algorithms and selected the NL-means, Laplacian filter and histogram equalization to be used as preprocessing techniques. Experimental results showed that using a suitable preprocessing scheme would produce a better segmentation process.

Cite This Paper

Ahmed Basil Al-Othman,Zohair Al-Ameen,Ghazali Bin Sulong,"Improving the MRI Tumor Segmentation Process Using Appropriate Image Processing Techniques", IJIGSP, vol.6, no.2, pp.23-29, 2014. DOI: 10.5815/ijigsp.2014.02.03


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