File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1007/11744085_31
- Scopus: eid_2-s2.0-33745845440
- Find via
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Database-guided simultaneous multi-slice 3D segmentation for volumetric data
Title | Database-guided simultaneous multi-slice 3D segmentation for volumetric data |
---|---|
Authors | |
Issue Date | 2006 |
Citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, v. 3954 LNCS, p. 397-409 How to Cite? |
Abstract | Automatic delineation of anatomical structures in 3-D volumetric data is a challenging task due to the complexity of the object appearance as well as the quantity of information to be processed. This makes it increasingly difficult to encode prior knowledge about the object segmentation in a traditional formulation as a perceptual grouping task. We introduce a fast shape segmentation method for 3-D volumetric data by extending the 2-D database-guided segmentation paradigm which directly exploits expert annotations of the interest object in large medical databases. Rather than dealing with 3-D data directly, we take advantage of the observation that the information about position and appearance of a 3-D shape can be characterized by a set of 2-D slices, Cutting these multiple slices simultaneously from the 3-D shape allows us to represent and process 3-D data as efficiently as 2-D images while keeping most of the information about the 3-D shape. To cut slices consistently for all shapes, an iterative 3-D non-rigid shape alignment method is also proposed for building local coordinates for each shape. Features from all the slices are jointly used to learn to discriminate between the object appearance and background and to learn the association between appearance and shape. The resulting procedure is able to perform shape segmentation in only a few seconds. Extensive experiments on cardiac ultrasound images demonstrate the algorithm's accuracy and robustness in the presence of large amounts of noise. © Springer-Verlag Berlin Heidelberg 2006. |
Persistent Identifier | http://hdl.handle.net/10722/326711 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Wei | - |
dc.contributor.author | Georgescu, Bogdan | - |
dc.contributor.author | Zhou, Xiang Scan | - |
dc.contributor.author | Krishuan, Sriram | - |
dc.contributor.author | Ma, Yi | - |
dc.contributor.author | Comaniciu, Dorin | - |
dc.date.accessioned | 2023-03-31T05:25:59Z | - |
dc.date.available | 2023-03-31T05:25:59Z | - |
dc.date.issued | 2006 | - |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, v. 3954 LNCS, p. 397-409 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/326711 | - |
dc.description.abstract | Automatic delineation of anatomical structures in 3-D volumetric data is a challenging task due to the complexity of the object appearance as well as the quantity of information to be processed. This makes it increasingly difficult to encode prior knowledge about the object segmentation in a traditional formulation as a perceptual grouping task. We introduce a fast shape segmentation method for 3-D volumetric data by extending the 2-D database-guided segmentation paradigm which directly exploits expert annotations of the interest object in large medical databases. Rather than dealing with 3-D data directly, we take advantage of the observation that the information about position and appearance of a 3-D shape can be characterized by a set of 2-D slices, Cutting these multiple slices simultaneously from the 3-D shape allows us to represent and process 3-D data as efficiently as 2-D images while keeping most of the information about the 3-D shape. To cut slices consistently for all shapes, an iterative 3-D non-rigid shape alignment method is also proposed for building local coordinates for each shape. Features from all the slices are jointly used to learn to discriminate between the object appearance and background and to learn the association between appearance and shape. The resulting procedure is able to perform shape segmentation in only a few seconds. Extensive experiments on cardiac ultrasound images demonstrate the algorithm's accuracy and robustness in the presence of large amounts of noise. © Springer-Verlag Berlin Heidelberg 2006. | - |
dc.language | eng | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.title | Database-guided simultaneous multi-slice 3D segmentation for volumetric data | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/11744085_31 | - |
dc.identifier.scopus | eid_2-s2.0-33745845440 | - |
dc.identifier.volume | 3954 LNCS | - |
dc.identifier.spage | 397 | - |
dc.identifier.epage | 409 | - |
dc.identifier.eissn | 1611-3349 | - |