Database-Guided Simultaneous Multi-slice 3D Segmentation for Volumetric Data View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2006

AUTHORS

Wei Hong , Bogdan Georgescu , Xiang Sean Zhou , Sriram Krishnan , Yi Ma , Dorin Comaniciu

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. More... »

PAGES

397-409

References to SciGraph publications

  • 1995. A desicion-theoretic generalization of on-line learning and an application to boosting in COMPUTATIONAL LEARNING THEORY
  • 1998. Active appearance models in COMPUTER VISION — ECCV’98
  • 1988-01. Snakes: Active contour models in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2000-06. A Trainable System for Object Detection in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/11744085_31

    DOI

    http://dx.doi.org/10.1007/11744085_31

    DIMENSIONS

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