Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 28, Issue 1, Pages 119-134Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2006.19
Keywords
optimal surface; medical image segmentation; graph algorithms; graph cut; minimum s-t cut; geometric constraint
Funding
- NHLBI NIH HHS [R01 HL063373-07, R01 HL063373-05A1, R01 HL064368-03, R01 HL063373-02, R01 HL064368-01, R01 HL064368-02, R01 HL064368-05, R01 HL064368-04, R01 HL071809, R01 HL071809-03, R01 HL063373-01, R01 HL064368-06A1, R01 HL064368-08, R01 HL063373-03, R01 HL063373-04, R01 HL064368-09, R01 HL064368-07, R01 HL071809-01A1, R01 HL064368, R01 HL063373, R01 HL064368-03S1, R01 HL063373-06, R01 HL071809-02, R01-HL64368, R01 HL071809-04] Funding Source: Medline
- NIBIB NIH HHS [R01 EB004640-02, R01 EB004640-03, R01 EB004640, R01 EB004640-01A2] Funding Source: Medline
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL071809, R01HL064368, R01HL063373] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB004640] Funding Source: NIH RePORTER
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Efficient segmentation of globally optimal surfaces representing object boundaries in volumetric data sets is important and challenging in many medical image analysis applications. We have developed an optimal surface detection method capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations. The method solves the surface segmentation problem by transforming it into computing a minimum s-t cut in a derived arc-weighted directed graph. The proposed algorithm has a low-order polynomial time complexity and is computationally efficient. It has been extensively validated on more than 300 computer-synthetic volumetric images, 72 CT-scanned data sets of different-sized plexiglas tubes, and tens of medical images spanning various imaging modalities. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional image segmentation.
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