Accurate image acquisition techniques and analysis protocols for a reliable characterization of tissue engineering scaffolds are yet to be well defined. To this aim, the most promising imaging technique seems to be the X-ray computed microtomography (μ-CT). However critical issues of the analysis process deal with the representativeness of the selected Volume of Interest (VOI) and, most significantly, its segmentation. This article presents an image analysis protocol that computes a set of quantitative descriptors suitable for characterizing the morphology and the micro-architecture of alginate/hydroxyapatite bone tissue engineering scaffolds. Considering different VOIs extracted from different μ-CT datasets, an automated segmentation technique is suggested and compared against a manual segmentation. Variable sizes of VOIs are also considered in order to assess their representativeness. The resulting image analysis protocol is reproducible, parameter-free and it automatically provides accurate quantitative information in addition to the simple qualitative observation of the acquired images.

Automated quantitative characterization of alginate/hydroxyapatite bone tissue engineering scaffolds by means of micro-CT image analysis

BRUN, FRANCESCO;TURCO, GIANLUCA;ACCARDO, AGOSTINO;PAOLETTI, SERGIO
2011

Abstract

Accurate image acquisition techniques and analysis protocols for a reliable characterization of tissue engineering scaffolds are yet to be well defined. To this aim, the most promising imaging technique seems to be the X-ray computed microtomography (μ-CT). However critical issues of the analysis process deal with the representativeness of the selected Volume of Interest (VOI) and, most significantly, its segmentation. This article presents an image analysis protocol that computes a set of quantitative descriptors suitable for characterizing the morphology and the micro-architecture of alginate/hydroxyapatite bone tissue engineering scaffolds. Considering different VOIs extracted from different μ-CT datasets, an automated segmentation technique is suggested and compared against a manual segmentation. Variable sizes of VOIs are also considered in order to assess their representativeness. The resulting image analysis protocol is reproducible, parameter-free and it automatically provides accurate quantitative information in addition to the simple qualitative observation of the acquired images.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2385609
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