The objective of this study was to compare the facial morphologies of an adult Chinese population to a Houstonian white population. Three-dimensional (3D) images were acquired via a commercially available stereophotogrammetric camera system, 3dMDface™. Using the system, 100 subjects from a Houstonian population and 71 subjects from a Chinese population were photographed. A complex mathematical algorithm was performed to generate a composite facial average (one for males and one for females) for each subgroup. The computer-generated facial averages were then superimposed based on a previously validated superimposition method. The facial averages were evaluated for differences. Distinct facial differences were evident between the subgroups evaluated. These areas included the nasal tip, the peri-orbital area, the malar process, the labial region, the forehead, and the chin. Overall, the mean facial difference between the Chinese and Houstonian female averages was 2.73 ± 2.20 mm, while the difference between the Chinese and Houstonian males was 2.83 ± 2.20 mm. The percent similarity for the female population pairings and male population pairings were 10.45% and 12.13%, respectively. The average adult Chinese and Houstonian faces possess distinct differences. Different populations and ethnicities have different facial features and averages that should be considered in the planning of treatment.
During the clinical evaluation of a patient, the clinician compiles a problem list consisting of skeletal, dental, and soft tissue discrepancies. Each of these three tissues can present problems in any of three dimensions and with a range of severities. Once an orthodontist has a complete problem list with appropriate diagnoses, treatment objectives are compiled. Often due to the nature and extent of the orthodontic problems, the treatment objectives may not fully address all of the orthodontic problems a patient presents with. An orthodontist must prioritize the orthodontic problems and make compromises to achieve the best overall result. In determining orthodontic priorities the orthodontist must consider the patient’s chief concern. Williams et al. reported that ‘appearance’ was the major motivation for patients seeking orthodontic treatment. To treat patients appropriately orthodontists must understand the importance of facial aesthetics in overall appearance, be able to collect meaningful and accurate data in regards to facial aesthetic, be able to interpret that data, and understand how different treatment modalities can affect facial aesthetics.
In the society in which we live today, an increased emphasis is placed on how people look. Facial aesthetics is an essential component of overall appearance. According to a review by Faure et al., facial appearance affects significant aspects of our lives, including those with whom we choose to associate. These seemingly unrelated characteristics include the idea that nicer looking individuals are nicer people, more intelligent, and have a higher educational potential.
This increased emphasis the general population places on facial aesthetics indicates the importance an orthodontist should place on improving facial aesthetics as part of the proposed orthodontic treatment plan. When prioritizing treatment objectively, improving facial aesthetics should be at the top of the list. Although at times emphasis on facial aesthetics has been misplaced in orthodontics, the importance of facial aesthetics in orthodontics is not new. In his review of orthodontic history, Wahl wrote, “Now it appears that facial aesthetics is again at the forefront as we realize why patients come to us in the first place”. From diagnosis, to treatment planning, to treatment execution, facial aesthetics must influence every aspect of an orthodontist’s decision-making.
Recent advances in technology have allowed clinicians to use advanced three-dimensional (3D) technology to collect facial aesthetic data in three dimensions and to evaluate those findings objectively and quantitatively using powerful computer software. These new technologies represent a significant advancement in an orthodontist’s ability to evaluate facial aesthetics. Many orthodontists now routinely use 3D images as a part of their armamentarium for diagnosis and treatment evaluation, because of their usefulness. 3D images have the advantage of being able to provide the orthodontist with a more accurate representation of facial soft tissue and morphologies, and can be a tool used to compare and predict orthodontic outcomes. Additionally, digital 3D images can enhance the captured images for improved evaluation and can be easily transferred for communication with colleagues and patients.
In a review article, Bishara et al. also emphasize the importance of ethnic variation and provide the following examples of the variation of average facial aesthetics between different ethnic groups: (1) Mexicans exhibit more dental and skeletal protrusion than white Americans, and Mexican girls have a more protrusive mandible than girls from Iowa. (2) Compared with white Americans, Iranians show a flat skeletal profile with an increased lip convexity because of dental protrusion. (3) Japanese have a more protrusive dentition, an increased lower facial height, and a steeper mandibular plane than white Americans. (4) Compared with white Americans, black South Africans have a more protrusive maxilla and an increased ANB angle (A point, Nasion, B point), and a greater labial inclination of the mandibular incisors. South African blacks have less protrusive incisors than American blacks. (5) White children from the southern USA have more prominent incisors than white children from the northern USA. Although some of these comparisons may have limited value in a clinical setting, this study clearly demonstrates the wide variation in facial morphology among different ethnicities.
With this new found ability of orthodontists to collect accurate facial dimensions during initial exams, along with the understanding that an aesthetic face is often an average face, a need has been created for databases of average facial soft tissue dimensions. These databases can then be used as a comparison to the facial soft tissue dimensions of an individual patient. To be truly useful these databases must be segmented into various ethnic groups to account for the unique dimension ethnicity contributes to facial aesthetics.
Until now, there has been very little published with regards to the use of 3D images to compare facial morphologies. This study was performed in order to evaluate the differences in facial morphologies between two distinct populations from different areas of the world.
Materials and methods
Subjects were selected from two different study sites. One group was composed of individuals from Xi’an Jiaotong University in Xi’an China. The other group was from the University of Texas Health Science Center Houston. In total, 171 subjects from the two population groups were selected: 50 males and 50 females from Houston, and 32 males and 39 females from China. The necessary institutional review board approval was acquired. All subjects were mainly dental students; they were asked to participate in the study and a questionnaire was used to determine racial type.
The subjects were accepted as participants if they satisfied the following inclusion criteria: (1) white descent (Houston group) and Chinese descent (Chinese group); (2) aged 18–30 years; (3) no adverse skeletal deviations (mild Class II and IIIs were included); (4) body mass index (BMI) of <25 kg/m 2 ; (5) no gross craniofacial anomalies.
The imaging system used in this study was the portable 3dMDface™ system, a structured light system using a combination of stereophotogrammetry and the structured light technique. This system uses a multi-camera configuration, with three cameras on each side (one colour and two infra-red), which captures photo-realistic quality pictures. A random light pattern is projected onto a subject and an image is captured with multiple synchronized digital cameras set at various angles in an optimum configuration. This system is able to capture full facial images from ear to ear and under the chin in 1.5 ms at the highest resolution. The manufacturer accuracy is less than 0.5 mm and the quoted clinical accuracy is 1.5% of the total observed variance. 3D surface images captured by surface acquisition systems are highly repeatable, and 3D landmark data can be acquired with a high degree of precision. Images taken from the 3dMDface system were analyzed and viewed on a computer using the 3dMDpatient™ Software Platform ( Fig. 1 ).
All images were standardized using a portable 3D imaging device by 3dMD. Each patient was seated on a chair with his/her face centred on a computer screen. 3D images were then developed by means of stereophotogrammetry and structured light technology. Each image took approximately 1.50 ms to capture and was transferred to the 3dMD software, which converted the data into a 3D image.
Processing of facial shells
All of the images acquired were analyzed by way of Rapidform 2006 Plus Pack 2 software (RF6 PP2). As part of the computer analysis, the data were processed before analysis in order to obtain an image that had a preserved shape, surface, and volume, using custom macros for the RF6 as described previously. As a result of this processing procedure, one facial shell was created for each subject. Furthermore, this allowed us to create and compare facial averages of the groups involved.
Average face constructions
Utilizing the computer-generated facial shells, facial averages were constructed for the Chinese males (CHI-M), the Chinese females (CHI-F), the Houstonian males (HOU-M), and the Houstonian females (HOU-F). The process of creating average facial constructs was carried out with a previously validated software sub-routine as part of the RF6 software. The steps required to produce an average face have been reported previously. Essentially, images were aligned on a best-fit algorithm and averaged.
The four facial averages that were generated as stated above (CHI-M, CHI-F, HOU-M, and HOU-F) were superimposed onto one another and compared by means of a specialized computer-assisted technique in order to compare and analyze differences in facial morphology. The superimpositions were carried out by selecting various points or landmarks on each of the corresponding facial average images (inner and outer canthi, tip of the nose, and corners of the mouth). Using fine registration, the RF6 software then aligned the two facial average shells by means of finding a best-fit of the two images. The following comparisons were made between subgroups: (1) CHI-F vs. CHI-M; (2) HOU-F vs. HOU-M; (3) CHI-F vs. HOU-F; (4) CHI-M vs. HOU-M; (5) CHI-F vs. HOU-M; (6) CHI-M vs. HOU-F.
The parameters utilized were linear measurements, colour histograms, and surface areas/shapes, as summarized below.
Linear measurements representing the mean differences between two surface shells were recorded in millimetres. This value represents the sum total of all differences recorded between overlapping surfaces of two shells. Additionally, this value could be used as an indicator of the best fit between two shells, as well as be used as an indicator of where changes/differences exist between the corresponding shells.
Colour histograms were produced using the RF6 software and show the areas of change that occurred between the average facial shells. In these histograms, blue areas show ‘negative’ changes and red areas show ‘positive’ changes.
Surface areas and shapes were automatically generated by RF6. These shapes were obtained when a previous tolerance of 0.425 mm was applied to the paired surface shell studies. The value of 0.425 is a mean value obtained from a reproducibility study of facial pose and is used for average faces. The areas that corresponded to 0.425 mm were deemed to be similar surfaces, while surface areas above this tolerance showed up as surface shapes and colour deviations.
The mean age of the sample population was 26.5 ± 3.3 years.
Average faces were constructed for each of the four subgroups: CHI-M, CHI-F, HOU-M, and HOU-F (Figs 2 and 3 ). These averages were later used as a means of comparison between subgroups. Figures 2 and 3 show fontal, oblique, and profile views of each of the four subgroups.
Differences in the average absolute linear measurements ranged from 1.12 mm (CHI-F vs. CHI-M) to 3.45 mm (CHI-F vs. HOU-M), as shown in Table 1 . Of special note are the differences between the gender-specific groups: 2.83 mm for the comparison of Chinese and Houstonian male subgroups, and 2.73 mm for the comparison of Chinese and Houstonian female subgroups.
|Average distance (mm)||SD (mm)||Maximum distance (mm)|
|CHI-F vs. CHI-M||1.11647||0.73701||7.24811|
|CHI-F vs. HOU-F||2.73444||2.20215||11.58727|
|CHI-F vs. HOU-M||3.45219||2.80995||9.5219|
|CHI-M vs. HOU-F||2.677||2.37||11.88666|
|CHI-M vs. HOU-M||2.83332||2.19631||10.839|
|HOU-M vs. HOU-F||1.53833||1.49144||8.60686|