A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation

Abstract

Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images.

Land use Detection in Nusajaya using Higher-Order Modified Geodesic Active Contour Model

“…The Chan-Vese (CV) model subsequently does not generally segment images with intensity inhomogeneity. Therefore, the local information of the given image should be considered to segment the inhomogeneity images [12], [13], [16]. Some researchers have proposed an essential model to extract local information from inhomogeneity images.…”

“…Some researchers have proposed an essential model to extract local information from inhomogeneity images. The proposed model is known as an active contour model based on local image fitting [12], [13], [16]. The other proposed model that considered the local information from inhomogeneity images is known as the local signed pressure force (SPF) function which utilized the advantages from both GAC and CV model [11], [17], [18].…”