Abstract: An image of an anatomical structure can be analyzed to determine an enclosing three-dimensional boundary when the anatomical structure is filled with two substances, such as air and a fluid. Various techniques can be used to determine the enclosing boundary including: analyzing the virtual structure to segment the structure into air and fluid pockets, determining if there are multiple fluid pockets whose surface touches a single air-fluid boundary, determining a separate threshold for respective fluid pockets, resegmenting the virtual anatomical structure using the separate threshold for different fluid pockets, forming a hierarchical pocket tree which represents the relationship between the fluid and air pockets, pruning the pocket tree based on various criteria which corresponds to deleting those pruned portions from the virtual anatomical structure, and resegmenting the remaining virtual anatomical structure using one or more of fuzzy connectedness, two-dimensional gap filling, and level set segmentation.

Abstract: Candidate anomalies in an anatomical structure are processed for classification. For example, false positives can be reduced by techniques related to the anomaly's neck, wall thickness associated with the anomaly, template matching performed for the anomaly, or some combination thereof. The various techniques can be combined for use in a classifier, which can determine whether the anomaly is of interest. For example, a computed tomography scan of a colon can be analyzed to determine whether a candidate anomaly is a polyp. The technologies can be applied to a variety of other scenarios involving other anatomical structures.

Abstract: An image of an anatomical structure can be analyzed to determine an enclosing three-dimensional boundary when the anatomical structure is filled with two substances, such as air and a fluid. Various techniques can be used to determine the enclosing boundary including: analyzing the virtual structure to segment the structure into air and fluid pockets, determining if there are multiple fluid pockets whose surface touches a single air-fluid boundary, determining a separate threshold for respective fluid pockets, resegmenting the virtual anatomical structure using the separate threshold for different fluid pockets, forming a hierarchical pocket tree which represents the relationship between the fluid and air pockets, pruning the pocket tree based on various criteria which corresponds to deleting those pruned portions from the virtual anatomical structure, and resegmenting the remaining virtual anatomical structure using one or more of fuzzy connectedness, two-dimensional gap filling, and level set segmentation.

Abstract: An image of an anatomical structure can be analyzed to determine an enclosing three-dimensional boundary when the anatomical structure is filled with two substances, such as air and a fluid. Various techniques can be used to determine the enclosing boundary including: analyzing the virtual structure to segment the structure into air and fluid pockets, determining if there are multiple fluid pockets whose surface touches a single air-fluid boundary, determining a separate threshold for respective fluid pockets, resegmenting the virtual anatomical structure using the separate threshold for different fluid pockets, forming a hierarchical pocket tree which represents the relationship between the fluid and air pockets, pruning the pocket tree based on various criteria which corresponds to deleting those pruned portions from the virtual anatomical structure, and resegmenting the remaining virtual anatomical structure using one or more of fuzzy connectedness, two-dimensional gap filling, and level set segmentation.

Abstract: Candidate anomalies in an anatomical structure are processed for classification. For example, false positives can be reduced by techniques related to the anomaly's neck, wall thickness associated with the anomaly, template matching performed for the anomaly, or some combination thereof. The various techniques can be combined for use in a classifier, which can determine whether the anomaly is of interest. For example, a computed tomography scan of a colon can be analyzed to determine whether a candidate anomaly is a polyp. The technologies can be applied to a variety of other scenarios involving other anatomical structures.

Abstract: Candidate anomalies in an anatomical structure are processed for classification. For example, false positives can be reduced by techniques related to the anomaly's neck, wall thickness associated with the anomaly, template matching performed for the anomaly, or some combination thereof. The various techniques can be combined for use in a classifier, which can determine whether the anomaly is of interest. For example, a computed tomography scan of a colon can be analyzed to determine whether a candidate anomaly is a polyp. The technologies can be applied to a variety of other scenarios involving other anatomical structures.

Abstract: Candidate anomalies in an anatomical structure are processed for classification. For example, false positives can be reduced by techniques related to the anomaly's neck, wall thickness associated with the anomaly, template matching performed for the anomaly, or some combination thereof. The various techniques can be combined for use in a classifier, which can determine whether the anomaly is of interest. For example, a computed tomography scan of a colon can be analyzed to determine whether a candidate anomaly is a polyp. The technologies can be applied to a variety of other scenarios involving other anatomical structures.

Abstract: An image of an anatomical structure can be analyzed to determine an enclosing three-dimensional boundary when the anatomical structure is filled with two substances, such as air and a fluid. Various techniques can be used to determine the enclosing boundary including: analyzing the virtual structure to segment the structure into air and fluid pockets, determining if there are multiple fluid pockets whose surface touches a single air-fluid boundary, determining a separate threshold for respective fluid pockets, resegmenting the virtual anatomical structure using the separate threshold for different fluid pockets, forming a hierarchical pocket tree which represents the relationship between the fluid and air pockets, pruning the pocket tree based on various criteria which corresponds to deleting those pruned portions from the virtual anatomical structure, and resegmenting the remaining virtual anatomical structure using one or more of fuzzy connectedness, two-dimensional gap filling, and level set segmentation.

Abstract: Candidate anomalies in an anatomical structure are processed for classification. For example, false positives can be reduced by techniques related to the anomaly's neck, wall thickness associated with the anomaly, template matching performed for the anomaly, or some combination thereof. The various techniques can be combined for use in a classifier, which can determine whether the anomaly is of interest. For example, a computed tomography scan of a colon can be analyzed to determine whether a candidate anomaly is a polyp. The technologies can be applied to a variety of other scenarios involving other anatomical structures.

Abstract: Candidate anomalies in an anatomical structure are processed for classification. For example, false positives can be reduced by techniques related to the anomaly's neck, wall thickness associated with the anomaly, template matching performed for the anomaly, or some combination thereof. The various techniques can be combined for use in a classifier, which can determine whether the anomaly is of interest. For example, a computed tomography scan of a colon can be analyzed to determine whether a candidate anomaly is a polyp. The technologies can be applied to a variety of other scenarios involving other anatomical structures.