Manual segmentation of structures for diagnosis and treatment of various diseases is a very time-consuming procedure. Therefore, some level of automation during the segmentation is desired, as it often significantly reduces the segmentation time. A typical solution is to allow manual interaction to steer the segmentation process, which is known as semiautomatic segmentation. In 2D, such interaction is usually achieved with click-and-drag operations, but in 3D a more sophisticated interface is called for. In this paper, we propose a semi-automatic Active Contour Modelling for the delineation of medical structures in 3D, tomographic images. Interaction is implemented with the employment of a 3D haptic device, which is used to steer the contour deformation towards the correct boundaries. In this way, valuable haptic feedback is provided about the 3D surface and its deformation. Experiments on simulated and real tracheal CT data showed that the proposed technique is an intuitive and effective segmentation mechanism. 1. Introduction Image segmentation in the medical field is an important step for the diagnosis and treatment of various diseases. In many cases, this task is performed manually [1, 2]. However, manual segmentation is widely acknowledged as being time consuming and intra- and interoperator dependent. Hence, some level of automation during the segmentation is desired, as it often significantly reduces the segmentation time. Medical image segmentation, in particular, is a very complex task, given the necessary precision required for object extraction and boundary delineation. A typical solution is to allow users to provide extra knowledge to or interfere with the segmentation process in order to refine the results yielded by the automatic steps, which is known as semiautomatic (or interactive) segmentation. The Active Contour Model (ACM) [3] is a well-known shape deformation algorithm to delineate structures in images, and several semiautomatic versions of this algorithm have been proposed in the literature [4]. ACMs minimise an energy function that controls the bending and stretching of a given initial contour and the attraction by image features. The expected result is that the contour matches the boundary of the structure of interest in the image. In 2D, the interface between user and algorithm is usually established with click/drag processes. However, if the data being segmented is three-dimensional, such as in 3D Computed Tomography (CT) images, a more refined interface is called for. The present work sets forth a 3D segmentation interface for
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