Adaptive kernel-based active contour
Gunjan Naik, Shubhangi Kelkar, Bhushan Garware, Aditya Abhyankar · International Journal of Computational Vision and Robotics · 2023
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.