Given a 2D/3D image containing one or more objects of interest, we address the general problem of segmenting these objects providing a control over the smoothness of their boundaries. The boundary smooth is performed by a nonlinear diffusion process which simultaneously detect and segment the objects, controlling their boundary fairness while preserving the edges of the objects. We denote this process as <i>regularized segmentation</i>. We address the efficient computation of regularized segmentation using semi-implicit and implicit complementary volume schemes. Finally, we show segmentation results on artificial and real-word 2D images and preliminary results in 3D.
Proc. SPIE. 4474, Advanced Signal Processing Algorithms, Architectures, and Implementations XI
KEYWORDS: Mathematical modeling, 3D acquisition, Detection and tracking algorithms, Image processing, Image restoration, 3D modeling, Image registration, Medical imaging, Motion models, 3D image processing
Image registration is a very common and important problem in several fields such as medical imaging, computer vision, simulation, etc. The aim of this contribution is to present a new mathematical partial differential equation (PDE)-model for the registration of two-dimensional (2D) and three-dimensional (3D), eventually noisy, images. Estimating the registration between two image data sets is here formulated as a motion estimation and evolution problem. Moreover we shortly review the PDE approaches which originated the proposed model. The model is based on ideas introduced for processing of space-time image sequences. The proposed algorithm can deal with small and large deformations, it also works in presence of noise and it is very fast. Computational results in processing of a variety of images including synthetic and medical images are presented.