Article: Adaptive kernel-based active contour Journal: International Journal of Computational Vision and Robotics (IJCVR) 2023 Vol.13 No.2 pp.202 - 218 Abstract: Geodesic active contour model (GACM) is a standard deterministic method for the segmentation of complex organ structures based on edge maps. For MRI images, the GACM performs poorly due to noise and weak edges, which might result from a low scanning period, low Tesla scanner machines, and other environmental conditions. The performance of GACM is getting affected due to constant edge detector kernels and based only on intensity values. To improve this performance, we have proposed a method involving adaptive kernels and phase-based edge detection called 'phase congruency'. The kernels used in phase congruency are log Gabor kernels for the calculation of edges. Instead of log Gabor kernels, we have proposed to use ICA kernels, which resemble similar anisotropic properties like log Gabor kernels and are also adaptive. This adaptive kernel-based phase congruency provides a robust edge map, to be used in GACM. Experimentation shows that when compared with state of art edge detection techniques, adaptive kernels enhance the weak as well as strong edges and improve the overall performance. Inderscience Publishers - linking academia, business and industry through research

Title: Adaptive kernel-based active contour

Authors: Gunjan Naik; Shubhangi Kelkar; Bhushan Garware; Aditya Abhyankar

Addresses: Department of Technology, Savitribai Phule Pune University, Pune 411007, India ' Persistent Systems Ltd., 9a, Aryabhata-Pingala, 12, Kashibai Khilare Path, Marg, Erandwane, Pune, Maharashtra 411004, India ' Persistent Systems Ltd., 9a, Aryabhata-Pingala, 12, Kashibai Khilare Path, Marg, Erandwane, Pune, Maharashtra 411004, India ' Department of Technology, Savitribai Phule Pune University, Pune 411007, India

Abstract: Geodesic active contour model (GACM) is a standard deterministic method for the segmentation of complex organ structures based on edge maps. For MRI images, the GACM performs poorly due to noise and weak edges, which might result from a low scanning period, low Tesla scanner machines, and other environmental conditions. The performance of GACM is getting affected due to constant edge detector kernels and based only on intensity values. To improve this performance, we have proposed a method involving adaptive kernels and phase-based edge detection called 'phase congruency'. The kernels used in phase congruency are log Gabor kernels for the calculation of edges. Instead of log Gabor kernels, we have proposed to use ICA kernels, which resemble similar anisotropic properties like log Gabor kernels and are also adaptive. This adaptive kernel-based phase congruency provides a robust edge map, to be used in GACM. Experimentation shows that when compared with state of art edge detection techniques, adaptive kernels enhance the weak as well as strong edges and improve the overall performance.

Keywords: active contour model; image segmentation; phase congruency; edge detection; geodesic active contour model; GACM.

DOI: 10.1504/IJCVR.2023.129439

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.2, pp.202 - 218

Received: 08 Aug 2021
Accepted: 25 Jan 2022
Published online: 09 Mar 2023 *

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Title: Adaptive kernel-based active contour

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