In this paper we propose a novel similarity metric and a method for deformable registration of two images for a specific clinical application. The basic assumption in almost all deformable registration approaches is that there exist explicit correspondences between pixels across the two images. This principle is used to design image (dis)similarity metrics, such as sum of squared differences (SSD) or mutual information (MI). This assumption is strongly violated, for instance, within specific regions of images from abdominal or pelvic area of a patient taken at two different time points. Nevertheless, in some clinical applications, it is required to compute a smooth deformation field for all the regions within the image including the boundaries of such regions. In this paper, we propose a deformable registration method, which utilizes a priori intensity distributions of the regions delineated on one of images to devise a new similarity measure that varies across regions of image to establish a smooth and robust deformation field. We present validation results of the proposed method in mapping bladder, prostate, and rectum contours of computer tomography (CT) volumes of 10 patient taken for prostate cancer radiotherapy treatment planning and verification.     «

In this paper we propose a novel similarity metric and a method for deformable registration of two images for a specific clinical application. The basic assumption in almost all deformable registration approaches is that there exist explicit correspondences between pixels across the two images. This principle is used to design image (dis)similarity metrics, such as sum of squared differences (SSD) or mutual information (MI). This assumption is strongly violated, for instance, within specific reg...     »