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Optimal segmentation of microcomputed tomographic images of porous tissue‐engineering scaffolds
Author(s) -
Rajagopalan Srinivasan,
Lu Lichun,
Yaszemski Michael J.,
Robb Richard A.
Publication year - 2005
Publication title -
journal of biomedical materials research part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.849
H-Index - 150
eISSN - 1552-4965
pISSN - 1549-3296
DOI - 10.1002/jbm.a.30498
Subject(s) - materials science , biomedical engineering , segmentation , artificial intelligence , computer science , porosity , pattern recognition (psychology) , biological system , computer vision , composite material , medicine , biology
The morphometric properties of the porous tissue‐engineering scaffolds play a dominant role in the initial cell attachment and subsequent tissue regeneration. These properties can be derived nondestructively with the use of quantitative analysis of high‐resolution microcomputed tomography (μCT) imaging of scaffolds. Accurate segmentation of these acquired images into solid and porous subspaces is critical to the integrity of morphometric analysis. The absence of a single image‐processing technique to provide such accurate separability immune to all the intricacies of the acquired data makes this seemingly simple task significantly error prone. Consequently, an optimal segmentation has to be selected by ranking the segmentations produced by a multiplicity of methods. This article proposes a robust, easy‐to‐implement, unambiguous, signal‐processing‐based, ground‐truth‐free, segmentation rating metric that correlates with visual acuity. With the use of this metric it is possible, for the first time, to threshold the data with a wide range of techniques and select automatically the technique that best delineates the acquired image. The proposed solution has been extensively tested on μCT images of scaffolds fabricated with biodegradable poly (propylene fumarate) (PPF) with the use of a solvent casting particulate leaching process. The approaches proposed and the results obtained may have profound implications for accurate image‐based characterization of tissue‐engineering scaffolds. © 2005 Wiley Periodicals, Inc. J Biomed Mater Res, 2005