The development and testing of Thai facial soft tissue thickness data in three-dimensional computerized forensic facial reconstruction

2021 ◽  
pp. 002580242110576
Author(s):  
Pagorn Navic ◽  
Patison Palee ◽  
Sangsom Prapayasatok ◽  
Sukon Prasitwattanaseree ◽  
Apichat Sinthubua ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Computer Animation ◽  
Three Dimensional ◽  
Facial Reconstruction ◽  
Forensic Investigation ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Thai Population ◽  
Soft Tissue Thickness ◽  
Virtual Face

Forensic facial reconstruction is a useful tool to assist the public in recognizing human remains, leading to positive forensic investigation outcomes. To reproduce a virtual face, facial soft tissue thickness is one of the major guidelines to reach the accuracy and reliability for three-dimensional computerized facial reconstruction, a method that is making a significant contribution to improving forensic investigation and identification. This study aimed to develop a facial soft tissue thickness dataset for a Thai population, and test its reliability in the context of facial reconstruction. Three-dimensional facial reconstruction was conducted on four skulls (2 males and 2 females, with ages ranging between 51 to 60 years). Two main tools of three-dimensional computer animation and modeling software—Blender and Autodesk Maya—were used to rebuild the three-dimensional virtual face. The three-dimensional coordinate ( x, y, z) cutaneous landmarks on the mesh templates were aligned homologous to the facial soft tissue thickness markers on the three-dimensional skull model. The final three-dimensional virtual face was compared to the target frontal photograph using face pool comparison. Four three-dimensional virtual faces were matched at low to moderate levels, ranging from 30% to 70% accuracy. These results demonstrate that the facial soft tissue thickness database of a Thai population applied in this study could be useful for three-dimensional computerized facial reconstruction purposes.

Repeatability of facial soft tissue thickness measurements for forensic facial reconstruction using X-ray images

2016 ◽  
Vol 49 (2) ◽  
pp. 134-141 ◽  
Author(s):  
Ozgur Bulut ◽  
Kahraman Gungor ◽  
Nicolle Thiemann ◽  
Ismail Hizliol ◽  
Safa Gurcan ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Facial Reconstruction ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
X Ray ◽  
Soft Tissue Thickness ◽  
Thickness Measurements

scholarly journals Facial Soft Tissue Thickness in Forensic Facial Reconstruction: Is it enough if Norms Set?

2015 ◽  
Vol 06 (05) ◽  
Author(s):  
Gayathri Ramesh Ramesh ◽  
Nagarajappa Gadiputi Sreedhar ◽  
Masineni Narayanappa
Keyword(s):  
Soft Tissue ◽  
Facial Reconstruction ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness

scholarly journals DEVELOPING A UNIFIED FACIAL TISSUE THICKNESS MODEL FROM NEUROIMAGING DATA FOR SRI LANKA USING STATISTICAL ANALYSIS AND IMAGE PROCESSING TECHNIQUES

2017 ◽  
Vol 5 (7) ◽  
pp. 354-362
Author(s):  
Lochana T. Batuwitage ◽  
Aparni Jayawardena ◽  
Himashi Sandamini ◽  
Roshan N. Rajapakse ◽  
Damitha D. Karunaratna ◽  
...  
Keyword(s):  
Soft Tissue ◽  
National Level ◽  
Facial Reconstruction ◽  
Sri Lankan ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Facial Profile ◽  
Soft Tissue Thickness ◽  
Neuroimaging Data ◽  
Processing Techniques

Facial soft tissue thickness (FSTT) is essential to forensic anthropologists for facial reconstruction- recreating a recognizable face from an unidentified skull and to plastic surgeons for treatment planning. Together with the age and sex of a person, the facial profile is related to facial soft tissue thickness, which is required for accurate facial reconstruction and recognition. Having such a facial profile in the national level is very important for a country since FSTT changes according to the geographical factors. In this paper we are presenting a review on literature associated with this topic describing the methods used for data collection, measuring FSTT and analyzing those values along with the method we are proposing to be followed in the research we are to conduct in the Sri Lankan context.

scholarly journals Facial Soft Tissue Thickness of Midline in an Iranian Sample: MRI Study

2017 ◽  
Vol 11 (1) ◽  
pp. 375-383 ◽  
Author(s):  
Masume Johari ◽  
Farzad Esmaeili ◽  
Hadi Hamidi
Keyword(s):  
Soft Tissue ◽  
Maxillary Sinusitis ◽  
Age Groups ◽  
Facial Reconstruction ◽  
Radiology Department ◽  
P Value ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Cross Sectional ◽  
Soft Tissue Thickness

Background and Aim: To identify human skeletal remains, different methods can be used and using these techniques, important data can be obtained. However, facial reconstruction is the last method to indentify unknown human faces which requires knowledge about facial soft tissue thickness in the different positions of the face. The present study determined the facial soft tissue thickness in the different landmark points on the MRI images of patients referred to Radiology Department of Shahid Madani Hospital. Materials and Methods: In this descriptive cross-sectional trial, MRI images of 179 patients (61 males, 118 females) in the age range of 18-76 years old who did not show any pathologic lesions, were selected. The measurements of the facial soft tissue were done on 12 landmark points on the midline area by two radiologist observers using specific software on the images. The differences in the soft tissue thickness in these landmark points were statistically analyzed by Mann-Whitney U (in term of gender) and Kruskal-Wallis tests (in terms of Body Mass Index [BMI] and age groups). P value less than 0.05 was considered statistically significant. The data were compared with the results of other studies. Results: The results obtained in the present study were higher than Turkish and American studies in most of the landmark points. Facial soft tissue thickness in most of the landmarks was more in males than females. In some of the landmarks, significant differences were found between emaciated, normal and overweight patients while in most cases, soft tissue thickness increased with the increased BMI. In some cases, significant differences were noted between soft tissue thickness values among the different age groups, in which the thickness increased or thinned with the increased age. Statistical Analysis: There were statistically significant associations between the presence and surface area of Haller cells and the occurrence of ipsilateral maxillary sinusitis. Neither the angulation of the uncinate process nor the size of the maxillary sinus ostium significantly correlates with the formation of maxillary sinusitis. Conclusion: The data achieved in the present study can be used for the facial reconstruction purposes in the Iranian population; however, the slight differences existing between the studied population and other subgroup races must be considered for accurate reconstructions.

A dense approach for computation of facial soft tissue thickness data

2021 ◽  
pp. 200460
Author(s):  
Diana Toneva ◽  
Silviya Nikolova ◽  
Stanislav Harizanov ◽  
Dora Zlatareva ◽  
Vassil Hadjidekov
Keyword(s):  
Soft Tissue ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness

Midsagittal facial soft tissue thickness norms in an adult Mediterranean population

2019 ◽  
Vol 294 ◽  
pp. 217.e1-217.e7 ◽  
Author(s):  
Fouad Ayoub ◽  
Maria Saadeh ◽  
Georges Rouhana ◽  
Ramzi Haddad
Keyword(s):  
Soft Tissue ◽  
Mediterranean Population ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness

“A NOVEL ATTEMPT IN ESTIMATION OF FETAL WEIGHT USING NEW SOFT TISSUE MEASUREMENTS”

2021 ◽  
pp. 1-3
Author(s):  
Sathyan Gnanasigamani ◽  
Sudhakar Vadivel ◽  
Bala Subramaniam ◽  
Sakthivel Raja Ganesan ◽  
Pradeebaa Thiyagarajan ◽  
...  
Keyword(s):  
Birth Weight ◽  
Soft Tissue ◽  
Strong Predictor ◽  
Three Dimensional ◽  
Fetal Weight ◽  
Ultrasound Measurement ◽  
Accurate Estimation ◽  
Tissue Thickness ◽  
Third Trimester ◽  
Soft Tissue Thickness

Background: The Accurate estimation of fetal weight is important in modern obstetrics. Currently, Hadlock's formula is used widely for fetal weight estimation, which includes BPD, AC, FL and HC. The correct plane of measurement of various standard parameters is difcult to obtain especially in third trimester. Hence soft tissue thicknesses of the fetus are tested for correlation with birth weight in this study. Materials & Methods: A prospective observational study conducted among 90 pregnant females referred for Ultrasound examination in the third trimester with an interval from the ultrasound scan to delivery of ≤7 days from 2019 to 2020. Results: The measurements of abdominal, fetal mid-thigh and mid-arm soft tissue thicknesses correlated well with birth weight in a high statistically signicant positive linear relationship. A new regression model developed out of the soft tissue thicknesses(FASTT, FMASTT, FTSTT) correlates better than the Hadlock's model and Sujitkumar Hiwale et al model (For Indian population) based on BPD, HC, AC and FL Conclusion: Ultrasound measurement of soft tissue thickness may prove to be a strong predictor of fetal weight essential for sonographic assessment of pregnancy. They are easy and simple hence fetal soft tissue thickness measurements, both two- and three-dimensional, may prove to be a diagnostic parameter that has as small an error rate as possible, is quick to use and reproducible by different examiners

Testing regression and mean model approaches to facial soft-tissue thickness estimation

2020 ◽  
pp. 002580242097701
Author(s):  
Tobias MR Houlton ◽  
Nicolene Jooste ◽  
Maryna Steyn
Keyword(s):  
Soft Tissue ◽  
Regression Model ◽  
South African ◽  
Goodness Of Fit ◽  
Regression Equations ◽  
Tissue Thickness ◽  
Facial Soft Tissue ◽  
Soft Tissue Thickness ◽  
Subject Specific ◽  
Model Approach

Average facial soft-tissue thickness (FSTT) databanks are continuously developed and applied within craniofacial identification. This study considered and tested a subject-specific regression model alternative for estimating the FSTT values for oral midline landmarks using skeletal projection measurements. Measurements were taken from cone-beam computed tomography scans of 100 South African individuals (60 male, 40 female; Mage = 35 years). Regression equations incorporating sex categories were generated. This significantly improved the goodness-of-fit ( r2-value). Validation tests compared the constructed regression models with mean FSTT data collected from this study, existing South African FSTT data, a universal total weighted mean approach with pooled demographic data and collection techniques and a regression model approach that uses bizygomatic width and maximum cranial breadth dimensions. The generated regression equations demonstrated individualised results, presenting a total mean inaccuracy (TMI) of 1.53 mm using dental projection measurements and 1.55 mm using cemento-enamel junction projection measurements. These slightly outperformed most tested mean models (TMI ranged from 1.42 to 4.43 mm), and substantially outperformed the pre-existing regression model approach (TMI = 5.12 mm). The newly devised regressions offer a subject-specific solution to FSTT estimation within a South African population. A continued development in sample size and validation testing may help substantiate its application within craniofacial identification.

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