MCA aided geodesic active contours for image segmentation with textures

Abstract

Models of geodesic active contour (GAC) cannot usually distinguish one morphological component from another under conditions of complex textures. This paper proposes a morphological component analysis (MCA) aided GAC, namely MCA-GAC. The central effort is to segment image objects accurately and overcome obstacles from the undesired textures during the contour evolution. MCA-GAC takes advantage of the iterative property of MCA and optimal sparse representation of curvelet for edges. Segmentation is accomplished by evolving MCA-GACs through curvelet scales and MCA iterations. MCA-GAC is testified under conditions of textures and additive Gaussian white random noise. Experimental results demonstrate that MCA-GAC has competitive and practical prospects in the tasks of segmentation.