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Adaptive Active Contour Model for Brain Tumor Segmentation

Adaptive Active Contour Model for Brain Tumor Segmentation

Gunjan Naik, Aditya Abhyankar, Bhushan Garware, Shubhangi Kelkar
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 17
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781683182122|DOI: 10.4018/IJCVIP.314947
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MLA

Naik, Gunjan, et al. "Adaptive Active Contour Model for Brain Tumor Segmentation." IJCVIP vol.12, no.1 2022: pp.1-17. http://doi.org/10.4018/IJCVIP.314947

APA

Naik, G., Abhyankar, A., Garware, B., & Kelkar, S. (2022). Adaptive Active Contour Model for Brain Tumor Segmentation. International Journal of Computer Vision and Image Processing (IJCVIP), 12(1), 1-17. http://doi.org/10.4018/IJCVIP.314947

Chicago

Naik, Gunjan, et al. "Adaptive Active Contour Model for Brain Tumor Segmentation," International Journal of Computer Vision and Image Processing (IJCVIP) 12, no.1: 1-17. http://doi.org/10.4018/IJCVIP.314947

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Abstract

For accurately diagnosing the severity of brain tumors in MRI images, Glioma segmentation is a significant step. The Glioma segmentation is due to noise and weak edges of organs in medical images. The geodesic active contour model (GACM) is a standard method for the segmentation of complex organ structures based on edge maps. The GACM performs poorly due to this noise and weak edges. So, the authors propose a method that uses adaptive kernels instead of a constant kernel for creating strong edge maps for GACM. The kernels used in phase congruency are Log Gabor kernels, which resemble similar anisotropic properties like Gabor kernels. They have replaced these with adaptive kernels. This adaptive kernel-based phase congruency provides a robust edge map to be used in GACM. Experimentation shows that when compared with state-of-the-art edge detection techniques, adaptive kernels enhance the weak as well as strong edges and improve the overall performance. Moreover, the proposed methodology substantially requires fewer parameters compared to existing ACM methods.