Abstract:
This paper provides methodology for fully automated model-based image segmentation. All information necessary to perform image segmentation is automatically derived from ...Show MoreMetadata
Abstract:
This paper provides methodology for fully automated model-based image segmentation. All information necessary to perform image segmentation is automatically derived from a training set that is presented in a form of segmentation examples. The training set is used to construct two models representing the objects-shape model and border appearance model. A two-step approach to image segmentation is reported. In the first step, an approximate location of the object of interest is determined. In the second step, accurate border segmentation is performed. The shape-variant Hough transform method was developed that provides robust object localization automatically. It finds objects of arbitrary shape, rotation, or scaling and can handle object variability. The border appearance model was developed to automatically design cost functions that can be used in the segmentation criteria of edge-based segmentation methods. The authors' method was tested in five different segmentation tasks that included 489 objects to be segmented. The final segmentation was compared to manually defined borders with good results [rms errors in pixels: 1.2 (cerebellum), 1.1 (corpus callosum), 1.5 (vertebrae), 1.4 (epicardial), and 1.6 (endocardial) borders]. Two major problems of the state-of-the-art edge-based image segmentation algorithms were addressed: strong dependency on a close-to-target initialization, and necessity for manual redesign of segmentation criteria whenever new segmentation problem is encountered.
Published in: IEEE Transactions on Medical Imaging ( Volume: 19, Issue: 10, October 2000)
DOI: 10.1109/42.887613
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Image Segmentation ,
- Object Location ,
- Edge Detection ,
- Object Borders ,
- Vertebrate ,
- Training Set ,
- Cerebellum ,
- Cost Function ,
- Corpus Callosum ,
- Segmentation Method ,
- Segmentation Algorithm ,
- Object Of Interest ,
- Segmentation Accuracy ,
- Segmentation Task ,
- Approximate Location ,
- Final Segmentation ,
- Segmentation Problem ,
- Hough Transform ,
- Endocardial Border ,
- Image Segmentation Algorithm ,
- Shape Model ,
- Number Of Landmarks ,
- Mean Vector ,
- Mean Shape ,
- Training Objective ,
- Shape Variation ,
- Epicardial Borders ,
- Manual Tracing ,
- Dynamic Programming ,
- Entire Image
- MeSH Terms
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Image Segmentation ,
- Object Location ,
- Edge Detection ,
- Object Borders ,
- Vertebrate ,
- Training Set ,
- Cerebellum ,
- Cost Function ,
- Corpus Callosum ,
- Segmentation Method ,
- Segmentation Algorithm ,
- Object Of Interest ,
- Segmentation Accuracy ,
- Segmentation Task ,
- Approximate Location ,
- Final Segmentation ,
- Segmentation Problem ,
- Hough Transform ,
- Endocardial Border ,
- Image Segmentation Algorithm ,
- Shape Model ,
- Number Of Landmarks ,
- Mean Vector ,
- Mean Shape ,
- Training Objective ,
- Shape Variation ,
- Epicardial Borders ,
- Manual Tracing ,
- Dynamic Programming ,
- Entire Image
- MeSH Terms