Assessment of similarity indices to quantify segmentation accuracy of scaffold images for tissue engineering

Srinivasan Rajagopalan; Richard Robb

doi:10.1117/12.594654

Assessment of similarity indices to quantify segmentation accuracy of scaffold images for tissue engineering

Srinivasan Rajagopalan, Richard Robb

Physiology & Biomedical Engineering

Research output: Contribution to journal › Conference article › peer-review

6 Scopus citations

Abstract

Existing similarity metrics to compare the accuracy of n-Dimensional image segmentation with the corresponding ground truth is restricted to a limited set of volume fractions which, by themselves, lack robustness. This paper introduces a comprehensive list of linear and non-linear similarity measures widely used in such diverse fields as ecology, toxicology and patent trending. These metrics based on the binary "absence/presence" data were computed for assessing the delineation of tissue engineering scaffold images into porous and polymeric space using a wide variety of thresholding techniques.

Original language	English (US)
Article number	189
Pages (from-to)	1636-1647
Number of pages	12
Journal	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	5747
Issue number	III
DOIs	https://doi.org/10.1117/12.594654
State	Published - 2005
Event	Medical Imaging 2005 - Image Processing - San Diego, CA, United States Duration: Feb 13 2005 → Feb 17 2005

Keywords

Delineation
Porous scaffolds
Segmentation
Similarity index
Tissue engineering

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Radiology Nuclear Medicine and imaging
Biomaterials

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10.1117/12.594654

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AB - Existing similarity metrics to compare the accuracy of n-Dimensional image segmentation with the corresponding ground truth is restricted to a limited set of volume fractions which, by themselves, lack robustness. This paper introduces a comprehensive list of linear and non-linear similarity measures widely used in such diverse fields as ecology, toxicology and patent trending. These metrics based on the binary "absence/presence" data were computed for assessing the delineation of tissue engineering scaffold images into porous and polymeric space using a wide variety of thresholding techniques.

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KW - Porous scaffolds

KW - Segmentation

KW - Similarity index

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Assessment of similarity indices to quantify segmentation accuracy of scaffold images for tissue engineering

Abstract

Keywords

ASJC Scopus subject areas

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