This manuscript tackles the registration of 2D biological images (histological sections or autoradiographs) to 2D images from the same or di#erent modalities (e.g., histology or MRI). The process of acquiring these images typically induces composite transformations that can be modeled as a number of rigid or a#ne local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. A hierarchical clustering algorithm then automatically partitions this field into a number of classes from which we extract independent pairs of sub-images. Finally, the pairs of sub-images are, independently, a#nely registered and a hybrid a#ne/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach under a variety of conditions, and discuss examples using real biomedical images, including MRI, histology and cryosection data.

Abstract — Medical Image Analysis plays a crucial role in the diagnosis, planning, control and follow-up of therapy. To be combined efficiently with medical robotics, Medical Image Analysis can be supported by the development of specific computational models of the human body operating at various levels. We describe in this article a hierarchy of these computational models, including the geometrical, physical and physiological levels, and illustrate their potential use in a number of advanced medical applications including image guided, robot-assisted and simulated medical interventions. We conclude this article with scientific perspectives.

affine registration of biological images for

This report tackles the registration of 2D biological images (histological sections or autoradiographs) to 2D images from the same or dierent modalities (e.g., histology or MRI). The process of acquiring these images typically induces composite transformations that we model as a number of rigid or ane local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair

We propose a fully automated method for locally registering 2-D multi-modal images. We model the images as a number of anatomically separate components, subject to aOEne transformations, and embedded in a deformable medium.

Abstract — The graphical user interface for an MR compatible robotic device has the capability of displaying oblique MR slices in 2D and a 3D virtual environment along with the representation of the robotic arm in order to swiftly complete the intervention. Using the advantages of the MR modality the device saves time and effort, is safer for the medical staff and is more comfortable for the patient. Index Terms — Surgical Robotics, Magnetic Resonance Imaging I.

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Abstract — Medical Image Analysis plays a crucial role in the diagnosis, planning, control and follow-up of therapy. To be combined efficiently with medical robotics, Medical Image Analysis can be supported by the development of specific computational models of the human body operating at various levels. We describe in this article a hierarchy of these computational models, including the geometrical, physical and physiological levels, and illustrate their potential use in a number of advanced medical applications including image guided, robot-assisted and simulated medical interventions. We conclude this article with scientific perspectives.