A fast level set image segmentation driven by a new region descriptor
Birane Abdelkader, Latifa Hamami · IET Image Processing · 2021
Abstract In order to deal with the intensities inhomogeneities and to overcome the effect of different types of noise in the image segmentation process, we have formulated a new level set function to implement a fast and robust active contour model. The proposed model was formulated by combining the SBGFRLS model and Legendre polynomials. With the aim of ensuring the segmentation accuracy and for dealing in the best way with the presence of noises and inhomogenous distribution of intensity, we define a local region descriptor for image intensities based on the Legendre polynomial. Instead of using the average intensity of the region, we regularised our level set function using a Gaussian filtering process. Experimental results on challenging images demonstrate the efficiency, robustness and the low cost and computational time of our model against the well‐known active contour models.