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This paper presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. We assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further we assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. We present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.

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Virtual reality (VR)-based surgical simulator systems offer a very elegant approach to enriching and enhancing traditional training in endoscopic surgery. However, while a number of VR simulator systems have been proposed and realized in the past few years, most of these systems are far from being able to provide a reasonably realistic surgical environment. We explore the current limits for realism and the approaches to reaching and surpassing those limits by describing and analyzing the most important components of VR-based endoscopic simulators. The feasibility of the proposed techniques is demonstrated on a modular prototype system that implements the basic algorithms for VR training in gynaecologic laparoscopy.

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