Method for segmenting chest CT image data using an anatomical model: preliminary results
| Autor: | J. Hiller, Matthew S. Brown, L.S. Wilson, J. Goldin, N.J. Mankovich, Michael F. McNitt-Gray, D.R. Aberie |
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| Rok vydání: | 1997 |
| Předmět: |
Lung Neoplasms
Computer science Fuzzy set ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION computer.software_genre Blackboard system Voxel Image Processing Computer-Assisted Humans Computer Simulation Computer vision Segmentation Electrical and Electronic Engineering Inference engine Lung Radiological and Ultrasound Technology business.industry Image segmentation Computer Science Applications Radiographic Image Enhancement Pulmonary Emphysema Knowledge base Feature (computer vision) Radiography Thoracic Artificial intelligence Tomography X-Ray Computed business computer Software |
| Zdroj: | Scopus-Elsevier |
| ISSN: | 0278-0062 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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