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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)
Differentiation of malignant and benign pulmonary nodules is of paramount clinical importance. Texture features of pulmonary nodules in CT images reflect a powerful character of the malignancy in addition to the geometry-related measures. This study first compared three well-known types of two-dimensional (2D) texture features (Haralick, Gabor, and local(More)
In this paper, we present a computer-aided detection (CAD) method to extract and use internal features to reduce false positive (FP) rate generated by surface-based measures on the inner colon wall in computed tomographic (CT) colonography. Firstly, a new shape description global curvature, which can provide an overall shape description of the colon wall,(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)
Image textures in computed tomography colonography (CTC) have great potential for differentiating non-neoplastic from neoplastic polyps and thus can advance the current CTC detection-only paradigm to a new level with diagnostic capability. However, image textures are frequently compromised, particularly in low-dose CT imaging. Furthermore, texture feature(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)
Cerebral perfusion x-ray computed tomography (PCT) imaging, which detects and characterizes the ischemic penumbra, and assesses blood-brain barrier permeability with acute stroke or chronic cerebrovascular diseases, has been developed extensively over the past decades. However, due to its sequential scan protocol, the associated radiation dose has raised(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)
This paper proposes a framework for detecting the suspected abnormal region of the bladder wall via magnetic resonance (MR) cystography. Volume-based features are used. First, the bladder wall is divided into several layers, based on which a path from each voxel on the inner border to the outer border is found. By using the path length to measure the wall(More)