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Segmentation of Preprocessed MR and CT Images Containing Tumors using Edge Detection and Watershed Segmentation

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IJCA Special Issue on Confluence 2012 - The Next Generation Information Technology Summit
© 2012 by IJCA Journal
CONFLUENCE - Number 1
Year of Publication: 2012
Authors:
Sonali Patil
V. R. Udupi

Sonali Patil and V R Udupi. Article: Segmentation of Preprocessed MR and CT Images Containing Tumors using Edge Detection and Watershed Segmentation. IJCA Special Issue on Confluence 2012 - The Next Generation Information Technology Summit CONFLUENCE(1):32-36, September 2012. Full text available. BibTeX

@article{key:article,
	author = {Sonali Patil and V. R. Udupi},
	title = {Article: Segmentation of Preprocessed MR and CT Images Containing Tumors using Edge Detection and Watershed Segmentation},
	journal = {IJCA Special Issue on Confluence 2012 - The Next Generation Information Technology Summit},
	year = {2012},
	volume = {CONFLUENCE},
	number = {1},
	pages = {32-36},
	month = {September},
	note = {Full text available}
}

Abstract

Segmentation of images aims at dividing areas corresponding to different objects. There are two approaches for image segmentation, one is based on discontinuities and other is based on similarities. These approaches can be used for enhancing and extracting the tumor area in MRI/CT images. In this paper Sobel and Extended Sobel edge operators are applied on the MRI / CT images containing tumors. It is noticed that the MR/CT images contain unwanted portions that make segmentation difficult. If such images are segmented without any preprocessing for removal of the unwanted portions, it results into over segmentation. In this paper, we propose to use Preprocessed MRI/CT image for the segmentation by using Sobel and extended Sobel operators. Results of both the methods on original and preprocessed images are displayed. The results of Watershed segmentation algorithm on original and preprocessed images are also displayed. It is observed that, the appropriate preprocessing of MR/CT images helps to significantly reduce the problem of over segmentation of these images still retaining the tumors.

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