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 regularized segmentation. 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.