We present an automated, knowledge-based method for segmenting chest computed tomography (CT) datasets. Anatomical knowledge including expected volume, shape, relative position, and X-ray attenuation of organs provides feature constraints that guide the segmentation process. Knowledge is represented at a high level using an explicit anatomical model. The model is stored in a frame-based semantic network and anatomical variability is incorporated using fuzzy sets. A blackboard architecture permits the data representation and processing algorithms in the model domain to be independent of those in the image domain. Knowledge-constrained segmentation routines extract contiguous three-dimensional (3-D) sets of voxels, and their feature-space representations are posted on the blackboard. An inference engine uses fuzzy logic to match image to model objects based on the feature constraints. Strict separation of model and image domains allows for systematic extension of the knowledge base. In preliminary experiments, the method has been applied to a small number of thoracic CT datasets. Based on subjective visual assessment by experienced thoracic radiologists, basic anatomic structures such as the lungs, central tracheobronchial tree, chest wall, and mediastinum were successfully segmented. To demonstrate the extensibility of the system, knowledge was added to represent the more complex anatomy of lung lesions in contact with vessels or the chest wall. Visual inspection of these segmented lesions was also favorable. These preliminary results suggest that use of expert knowledge provides an increased level of automation compared with low-level segmentation techniques. Moreover, the knowledge-based approach may better discriminate between structures of similar attenuation and anatomic contiguity. Further validation is required.
The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of WML are similar to those of gray matter. Intensity‐based statistical classification techniques misclassify some WML as gray matter and some gray matter as WML. We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the gray matter into the approximate shape of the patient's brain. The region of white matter was segmented after segmenting the cortex and deep gray matter structures. The cortex was identified by using a three‐dimensional, region‐growing algorithm that was constrained by anatomical, intensity gradient, and tissue class parameters. White matter and WML were then segmented without interference from gray matter by using a two‐class minimum‐distance classifier. Analysis of double‐echo spin‐echo MRI scans of 16 patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the cortex and deep gray matter structures provided anatomical context. This was found to improve the segmentation of MS lesions by allowing correct classification of the white matter region despite the overlapping tissue class distributions of gray matter and MS lesion. J Image Guid Surg 1:326–338 (1995). © 1996 Wiley‐Liss, Inc.
Searching information retrieval systems is a highly interactive, iterative process that cannot be understood simply by comparing the output of a search session (the "search product") to a query stated in advance. In this article, we examine evaluation goals and methods for studying information retrieval behavior, drawing examples from our own research and that of others. We limit our review to research that employs online monitoring, also known as transaction log analysis. Online monitoring is one of few methods that can capture detailed data on the search process at a reasonable cost; these data can be used to build quantitative models or to support qualitative interpretations of quantitative results. Monitoring is a data collection technique rather than a research design, and can be employed in experimental or field studies, whether alone or combined with other data collection methods. Based on the research questions of interest, the researcher must determine what variables to collect from each data source, which to treat as independent variables to manipulate, and which to treat as dependent variables to observe effects. Studies of searching behavior often treat search task and searcher characteristics as independent variables and may manipulate other independent variables specific to the research questions addressed. Search outcomes, time, and search paths frequently are treated as dependent variables. We discuss each of these sets of variables, illustrating them with sample results from the literature and from our own research. Our examples are drawn from the Science Library Catalog project, a 7-year study of children's searching behavior on an experimental retrieval system. We close with a brief discussion of the implications of these results for the design of information retrieval systems.
The nonlinear anisotropic diffusive process has shown the good property of eliminating noise while preserving the accuracy of edges and has been widely used in image processing. However, filtering depends on the threshold of the diffusion process, i.e., the cut-off contrast of edges. The threshold varies from image to image and even from region to region within an image. The problem compounds with intensity distortion and contrast variation. We have developed an adaptive diffusion scheme by applying the Central Limit Theorem to selecting the threshold. Gaussian distribution and Rayleigh distribution are used to estimate the distributions of visual objects in images. Regression under such distributions separates the distribution of the major object from other visual objects in a single-peak histogram. The separation helps to automatically determine the threshold. A fast algorithm is derived for the regression process. The method has been successfully used in filtering various medical images.
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