Hierarchical scale-based multiobject recognition of 3-D anatomical structures

Ulas Bagci*, Xinjian Chen, Jayaram K. Udupa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


Segmentation of anatomical structures from medical images is a challenging problem, which depends on the accurate recognition (localization) of anatomical structures prior to delineation. This study generalizes anatomy segmentation problem via attacking two major challenges: 1) automatically locating anatomical structures without doing search or optimization, and 2) automatically delineating the anatomical structures based on the located model assembly. For 1), we propose intensity weighted ball-scale object extraction concept to build a hierarchical transfer function from image space to object (shape) space such that anatomical structures in 3-D medical images can be recognized without the need to perform search or optimization. For 2), we integrate the graph-cut (GC) segmentation algorithm with prior shape model. This integrated segmentation framework is evaluated on clinical 3-D images consisting of a set of 20 abdominal CT scans. In addition, we use a set of 11 foot MR images to test the generalizability of our method to the different imaging modalities as well as robustness and accuracy of the proposed methodology. Since MR image intensities do not possess a tissue specific numeric meaning, we also explore the effects of intensity nonstandardness on anatomical object recognition. Experimental results indicate that: 1) effective recognition can make the delineation more accurate; 2) incorporating a large number of anatomical structures via a model assembly in the shape model improves the recognition and delineation accuracy dramatically; 3) ball-scale yields useful information about the relationship between the objects and the image; 4) intensity variation among scenes in an ensemble degrades object recognition performance.

Original languageEnglish (US)
Article number6112228
Pages (from-to)777-789
Number of pages13
JournalIEEE Transactions on Medical Imaging
Issue number3
StatePublished - Mar 2012
Externally publishedYes


  • Active shape model
  • graph-cut
  • image segmentation
  • intensity standardization
  • local scale
  • object recognition
  • principal component analysis
  • three-dimensional (3-D) shape models

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications


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