A New Method of Automatic Craniometric Landmarks Definition and Soft Tissue Thickness Measurement Based on MRI Data

Gorbenko, Iryna; Mikołajczyk, Krzysztof; Iarovyi, Iaroslav; Kubik, Tomasz; Kałużyński, K.

doi:10.1007/978-3-319-06593-9_11

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Such iterative registration maps the landmarks on the reference to the target. An automatic method for definition of the craniometric landmarks and soft tissues thickness measurement in these landmarks (Gorbenko et al, 2014) uses MRI data for craniometric landmarks extraction. The proposed technique isalso based on the non-rigid registration of the target image to the template.…”

Section: Related Workmentioning

confidence: 99%

Automatic Anthropological Landmarks Recognition and Measurements

Knyaz

Maksimov

Novikov

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Many anthropological researches require identification and measurement of craniometric and cephalometric landmarks which provide valuable information about the shape of a head. This information is necessary for morphometric analysis, face approximation, craniafacial identification etc. Traditional techniques use special anthropological tools to perform required measurements, identification of landmarks usually being made by an expert-anthropologist. Modern techniques of optical 3D measurements such as photogrammetry, computer tomography, laser 3D scanning provide new possibilities for acquiring accurate 2D and 3D data of high resolution, thus creating new conditions for anthropological data analysis. Traditional anthropological manual point measurements can be substituted by analysis of accurate textured 3D models, which allow to retrieve more information about studied object and easily to share data for independent analysis. The paper presents the deep learning technique for anthropological landmarks identification and accurate 3D measurements. Photogrammetric methods and their practical implementation in the automatic system for accurate digital 3D reconstruction of anthropological objects are described.

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Section: Related Workmentioning

confidence: 99%

Automatic Anthropological Landmarks Recognition and Measurements

Knyaz

Maksimov

Novikov

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Template design for craniometric landmarks identification

Gorbenko

Kałużyński

Mikołajczyk

2015

Advanced Mechatronics Solutions

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Global facial soft tissue thicknesses for craniofacial identification (2023): a review of 140 years of data since Welcker’s first study

Hona,

Stephan

2023

Int J Legal Med

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This year (2023) marks 140 years since the first publication of a facial soft tissue thickness (FSTT) study. Since 1883, a total of 139 studies have been published, collectively tallying > 220,000 tissue thickness measurements of > 19,500 adults. In just the last 5-years, 33 FSTT studies have been conducted. Herein, we add these data (plus an additional 20 studies) to the 2018 T-Table to provide an update of > 81,000 new datapoints to the global tallied facial soft tissue depths table. In contrast to the original 2008 T-Table, some notable changes are as follows: increased FSTTs by 3 mm at infra second molar (ecm2–iM2ʹ), 2.5 mm at gonion (go–goʹ), 2 mm at mid-ramus (mr–mrʹ), and 1.5 mm at zygion (zy–zyʹ). Rolling grand means indicate that stable values have been attained for all nine median FSTT landmarks, while six out of nine bilateral landmarks continue to show ongoing fluctuations, indicating further data collection at these landmarks holds value. When used as point estimators for individuals with known values across 24 landmarks (i.e., C-Table data), the updated grand means produce slightly less estimation error than the 2018 T-Table means (3.5 mm versus 3.6 mm, respectively). Future efforts to produce less noisy datasets (i.e., reduce measurement and sampling errors as much as possible between studies) would be useful.

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A New Method of Automatic Craniometric Landmarks Definition and Soft Tissue Thickness Measurement Based on MRI Data

Cited by 4 publications

References 9 publications

Automatic Anthropological Landmarks Recognition and Measurements

Automatic Anthropological Landmarks Recognition and Measurements

Template design for craniometric landmarks identification

Global facial soft tissue thicknesses for craniofacial identification (2023): a review of 140 years of data since Welcker’s first study

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