Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,818

The DOI matches its Crossref record.

A new method for the model‐independent assessment of thickness in three‐dimensional images

Hildebrand, T., Rüegsegger, P.·1997·Journal of Microscopy

Abstract

Three‐dimensional (3‐D) structural parameters derived from lower‐dimensional measurements using indirect morphometric methods may be strongly biased if the measured objects deviate from the assumed structure model. With the introduction of 3‐D microscopic measuring techniques it is possible to obtain a complete depiction of complex spatial structures. As a consequence, new 3‐D methods have recently been developed for the estimation of morphometric parameters such as volume, surface area and connectivity by direct processing of the 3‐D images. Structure thickness is an important morphometric parameter which is usually defined for specific structure models only. In this paper we propose a general thickness definition for arbitrary structures allowing us to calculate the mean structure thickness and the thickness distribution of 3‐D objects in a direct way and independently of an assumed structure model. Additionally, an efficient implementation for the practical usage of the method is described using distance transformation. The new method is applied to trabecular bone structures measured with a 3‐D micro‐computed tomography system.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1046/j.1365-2818.1997.1340694.x

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 1,818

Metadata

Authors
Hildebrand, T., & Rüegsegger, P.
Venue
Journal of Microscopy
Publisher
Wiley
Published
1997-01
Volume
185
Issue
1
Pages
67-75
ISSN
  • 0022-2720 (ISSN-L)
  • 1365-2818
OpenAlex
W1964983194
Cited by
1,818
References
0
FWCI
12.13
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A new method for the model‐independent assessment of thickness in three‐dimensional images. https://app.cochat.ai/papers/10.1046/j.1365-2818.1997.1340694.x
The paper
Hildebrand, T., & Rüegsegger, P. (1997). A new method for the model‐independent assessment of thickness in three‐dimensional images. Journal of Microscopy. https://doi.org/10.1046/j.1365-2818.1997.1340694.x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.