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A statistical method is developed to classify tissue types and to segment the corresponding tissue regions from relaxation time T(1 ), T(2), and proton density P(D) weighted magnetic resonance images. The method assumes that the distribution of image intensities associated with each tissue type can be expressed as a multivariate likelihood function of three(More)
In this paper, we introduce a concise and concrete definition of an accurate colon centerline and provide an efficient automatic means to extract the centerline and its associated branches (caused by a forceful touching of colon and small bowel or a deep fold in twisted colon lumen). We further discuss its applications on fly-through path planning and(More)
Noise, partial volume (PV) effect, and image-intensity inhomogeneity render a challenging task for segmentation of brain magnetic resonance (MR) images. Most of the current MR image segmentation methods focus on only one or two of the above-mentioned effects. The objective of this paper is to propose a unified framework, based on the maximum a posteriori(More)
In this paper, we propose a coupled level set (LS) framework for segmentation of bladder wall using T(1)-weighted magnetic resonance (MR) images with clinical applications to virtual cystoscopy (i.e., MR cystography). The framework uses two collaborative LS functions and a regional adaptive clustering algorithm to delineate the bladder wall for the wall(More)
We present an interactive navigation system for virtual colonoscopy, which is based solely on high performance volume rendering. Previous colonic navigation systems have employed either a surface rendering or a Z-buffer-assisted volume rendering method that depends on the surface rendering results. Our method is a fast direct volume rendering technique that(More)
Projection data acquired for image reconstruction of low-dose computed tomography (CT) are degraded by many factors. These factors complicate noise analysis on the projection data and render a very challenging task for noise reduction. In this study, we first investigate the noise property of the projection data by analyzing a repeatedly acquired(More)
We propose a novel multiscale penalized weighted least-squares (PWLS) method for restoration of low-dose computed tomography (CT) sinogram. The method utilizes wavelet transform for the multiscale or multi-resolution analysis on the sinogram. Specifically the Mallat-Zhong's wavelet transform is applied to decompose the sinogram to different resolution(More)
We present in this paper a method c alled 3D virtual colonoscopy, which is an alternative method t o existing procedures of imaging the mucosal surface o f the colon. Using 3D reconstruction of helical CT data and volume visualization techniques, we generate images of the inner surface o f t h e c olon as if the viewer's eyes were inside the colon. We also(More)
Reconstructing low-dose X-ray computed tomography (CT) images is a noise problem. This work investigated a penalized weighted least-squares (PWLS) approach to address this problem in two dimensions, where the WLS considers first- and second-order noise moments and the penalty models signal spatial correlations. Three different implementations were studied(More)
Electronic colon cleansing (ECC) aims to segment the colon lumen from a patient abdominal image acquired using an oral contrast agent for colonic material tagging, so that a virtual colon model can be constructed. Virtual colonoscopy (VC) provides fly-through navigation within the colon model, looking for polyps on the inner surface in a manner analogous to(More)