Robust noise region-based active contour model via local similarity factor for image segmentation. (January 2017) - Niu, Sijie; Chen, Qiang; de Sisternes, Luis; Ji, Zexuan; Zhou, Zeming; Rubin, Daniel L.
Abstract: Image segmentation using a region-based active contour model could present difficulties when its noise distribution is unknown. To overcome this problem, this paper proposes a novel region-based model for the segmentation of objects or structures in images by introducing a local similarity factor, which relies on the local spatial distance within a local window and local intensity difference to improve the segmentation results. By using this local similarity factor, the proposed method can accurately extract the object boundary while guaranteeing certain noise robustness. Furthermore, the proposed algorithm completely avoids the pre-processing steps typical of region-based contour model segmentation, resulting in a higher preservation of image details. Experiments performed on synthetic images and real word images demonstrate that the proposed algorithm, as compared with the state-of-art algorithms, is more efficient and robust to higher noise level manifestations in the images. Highlights: A local similarity factor was proposed to preserve noise robustness. The algorithm was adapted to eliminate the necessity of pre-processing steps. Experimental show that the algorithm provide more accurate segmentation results.