摘要： This study presents an efficient variational region-based active contour model for segmenting images without priori knowledge about the object or background. In order to handle intensity inhomogeneities and noise, we propose to integrate into the region-based local intensity model a global density distance inspired by the Bhattacharyya flow. The local term based on local information of segmented image allows the model to deal with bias field artifact, which arises in data acquisition processes. The global term, which is based on the density distance between the probability distribution functions of image intensity inside and outside the active contour, provides information for accurate segmentation, keeps the curve from spilling, and addresses noise in the image. Intensive 2D and 3D experiments on many imaging modalities of medical fields such as computed tomography, magnetic resonance imaging, and ultrasound images demonstrate the effectiveness of the model when dealing with images with blurred object boundary, intensity inhomogeneities, and noise. 其他題名： Machine Vision and Applications 出版者： Berlin/Heidelberg: Springer-Verlag 出版日期： 2012-11-01 出處： Machine vision and applications, 2012-11, Vol.23 (6), p.1159-1175 資源來源： EBSCOhost Academic Search Premier 版權： Springer-Verlag 2011 版權： Machine Vision and Applications is a copyright of Springer, (2011). All Rights Reserved. 識別號： ISSN: 0932-8092 識別號： EISSN: 1432-1769 識別號： DOI: 10.1007/s00138-011-0373-5
