An active contour model with adaptive weighted mean filtering and anisotropic diffusion filtering

Abstract

Active contour models (ACMs) are classic and effective methods for image segmentation. However, most existing ACMs cannot obtain satisfactory accuracy for segmenting images with inhomogeneous intensity caused by uneven illumination and noise. To address this issue, this work proposes an ACM with adaptive weighted mean filtering and anisotropic diffusion filtering (AWMAD). First, we propose a logarithmic-subtraction mechanism to decompose uneven illumination and noise of the original image, which can separate the high-frequency component with edge features from the original image. Then, we propose a fitted function based on adaptive weighted mean filtering to calculate the low frequency component, which suppresses the influence of uneven illumination on extraction of edge features. Moreover, we present a novel data driven term using anisotropic diffusion filtering and image entropy to suppress noise and enhance edge features. Finally, we construct an ACM based on our logarithmic-subtraction mechanism with the fitted function and data driven term, which eliminates convolution operations during the level set function iterations and accelerates the segmentation speed of AWMAD. Experimental results demonstrate that AWMAD has a high segmentation accuracy for segmenting images with uneven illumination and noise.