Edge Enhancement Based on an Active Contour Model for the Segmentation of Brain Tumors in MRI Images

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Published: Oct 31, 2021
DOI: https://doi.org/10.37936/ecti-eec.2021193.244942
Keywords:
Brain Tumor Edge Detection Active Contour Thresholding Morphological Operations Image Segmentation

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Mustafa Rashid Ismael
University of Babylon, Iraq

Abstract

Tumor segmentation is one of the most important tasks in brain image analysis due to the significant information contained in the tumor region. Therefore, many methods have been proposed during the last two decades for segmenting tumors in MRI images. In this paper, an automated method is proposed using an active contour model, created using edge sharpening, thresholding, and morphological operations. Four methods of edge detection are utilized in the sharpening process (Sobel, Roberts, Prewitt, and Canny) and their performance investigated in terms of Dice, Jaccard, and F1 score. The experiments implemented on BRATS datasets use both HGG and LGG images. The results of the study indicate that sharpening the edges using detection is essential for improving segmentation of the tumor region, especially when employed with an active contour model. The results demonstrate the effectiveness of the proposed method which outperforms some of the existing techniques.

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How to Cite
Rashid Ismael, M. (2021). Edge Enhancement Based on an Active Contour Model for the Segmentation of Brain Tumors in MRI Images. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 19(3), 353–362. https://doi.org/10.37936/ecti-eec.2021193.244942
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Vol. 19 No. 3 (2021): Regular Issue (October 2021)
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