3D Ear Model Research Articles

3D human ear modelling with parameterization technique and variation analysis

Anthropometry is vital to provide design references when seeking proper product fit. Nowadays, 3D anthropometry is widely used to provide more size and shape details for improving product designs. However, 3D ear anthropometry is still at an explorative stage, considering the complex ear morphology and other technical obstacles. The proposed research method in this study is applicable to analyse the 3D point cloud of the entire external ear. With the cross-parameterisation technique, the dataset was used to explore the morphological characteristics of the ear. Ear dimensions were automatically extracted and further analysed to explore the gender and symmetry differences using two-way ANOVA. The 3D ear models were investigated through Principal Component Analysis (PCA). The most significant variation was found in the helix and concha region, and the overall ear size is the second important factor determining ear variance. The statistical models were generated as 3D design references for ear-related products. Practitioner summary: This study revealed the morphological variations of the entire 3D external ear with a parameterised 3D ear dataset. Based on the PCA findings, a set of statistical models were generated as design references for product evaluation digitally or physically.

An automated parametric ear model to improve frugal 3D scanning methods for the advanced manufacturing of high-quality prosthetic ears

Ear prostheses are commonly used for restoring aesthetics to those suffering missing or malformed external ears. Traditional fabrication of these prostheses is labour intensive and requires expert skill from a prosthetist. Advanced manufacturing including 3D scanning, modelling and 3D printing has the potential to improve this process, although more work is required before it is ready for routine clinical use. In this paper, we introduce a parametric modelling technique capable of producing high quality 3D models of the human ear from low-fidelity, frugal, patient scans; significantly reducing time, complexity and cost. Our ear model can be tuned to fit the frugal low-fidelity 3D scan through; (a) manual tuning, or (b) our automated particle filter approach. This potentially enables low-cost smartphone photogrammetry-based 3D scanning for high quality personalised 3D printed ear prosthesis. In comparison to standard photogrammetry, our parametric model improves completeness, from (81 ± 5)% to (87 ± 4)%, with only a modest reduction in accuracy, with root mean square error (RMSE) increasing from (1.0 ± 0.2) mm to (1.5 ± 0.2) mm (relative to metrology rated reference 3D scans, n = 14). Despite this reduction in the RMS accuracy, our parametric model improves the overall quality, realism, and smoothness. Our automated particle filter method differs only modestly compared to manual adjustments. Overall, our parametric ear model can significantly improve quality, smoothness and completeness of 3D models produced from 30-photograph photogrammetry. This enables frugal high-quality 3D ear models to be produced for use in the advanced manufacturing of ear prostheses.

Fabrication of 3D-Printed Implant for Two-Stage Ear Reconstruction Surgery and Its Clinical Application.

Ear reconstruction is one of the most difficult areas in the field of reconstructive surgery. Due to limitations of the current practice, a novel method of auricular reconstruction is needed. Major advancements in three-dimensional (3D) printing technique have rendered the process of ear reconstruction more favorable. Herein, we present our experience in designing and clinically using 3D implants in both 1st and 2nd stage ear reconstruction surgery. After obtaining 3D CT data from each patient, a 3D geometric ear model was created using mirroring and segmentation processes. The 3D-printed implant design resembles but does not exactly match the normal ear shape, and can be inserted in harmony with the currently used surgical technique. The 2nd stage implant was designed to minimize dead space and support the posterior ear helix. The 3D implants were finally fabricated with a 3D printing system and used in ear reconstruction surgery in our institute. The 3D implants were manufactured for application to the currently used two-stage technique while maintaining the shape of the patient's normal ear. The implants were successfully used for ear reconstruction surgery in microtia patients. A few months later, the 2nd stage implant was used in the 2nd stage operation. The authors were able to design, fabricate, and apply patient-specific 3D-printed ear implants for 1st and 2nd stage ear reconstruction surgeries. This design, combined with 3D bioprinting technique, may be a future alternative for ear reconstruction.

Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model.

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application.

Fabrication of 3D Models for Microtia Reconstruction Using Smartphone-Based Technology.

Microtia reconstruction is technically challenging due to the intricate contours of the ear. It is common practice to use a two-dimensional tracing of the patient's normal ear as a template for the reconstruction of the affected side. Recent advances in three-dimensional (3D) surface scanning and printing have expanded the ability to create surgical models preoperatively. This study aims to describe a simple and affordable process to fabricate patient-specific 3D ear models for use in the operating room. Applied basic research on a novel 3D optical scanning and fabrication pathway for microtia reconstruction. Tertiary care university hospital. Optical surface scanning of the patient's normal ear was completed using a smartphone with facial recognition capability. The Heges application used the phone's camera to capture the 3D image. The 3D model was digitally isolated and mirrored using the Meshmixer software and printed with a 3D printer (MonopriceTM Select Mini V2) using polylactic acid filaments. The 3D model of the ear served as a helpful intraoperative reference and an adjunct to the traditional 2D template. Collectively, time for imaging acquisition, editing, and fabrication was approximately 3.5 hours. The upfront cost was around $210, and the recurring cost was approximately $0.35 per ear model. A novel, low-cost approach to fabricate customized 3D models of the ear is introduced. It is feasible to create individualized 3D models using currently available consumer technology. The low barrier to entry raises the possibility for clinicians to incorporate 3D printing into various clinical applications.

A virtual analysis of the precision and accuracy of 3-dimensional ear casts generated from smartphone camera images

Statement of problemThe anatomic complexity of the ear challenges conventional maxillofacial prosthetic rehabilitation. The introduction of specialized scanning hardware integrated into computer-aided design and computer-aided manufacturing (CAD-CAM) workflows has mitigated these challenges. Currently, the scanning hardware required for digital data acquisition is expensive and not readily available for prosthodontists in developing regions. PurposeThe purpose of this virtual analysis study was to compare the accuracy and precision of 3-dimensional (3D) ear models generated by scanning gypsum casts with a smartphone camera and a desktop laser scanner. Material and methodsSix ear casts were fabricated from green dental gypsum and scanned with a laser scanner. The resultant 3D models were exported as standard tessellation language (STL) files. A stereophotogrammetry system was fabricated by using a motorized turntable and an automated microcontroller photograph capturing interface. A total of 48 images were captured from 2 angles on the arc (20 degrees and 40 degrees from the base of the turntable) with an image overlap of 15 degrees, controlled by a stepper motor. Ear 1 was placed on the turntable and captured 5 times with smartphone 1 and tested for precision. Then, ears 1 to 6 were scanned once with a laser scanner and with smartphones 1 and 2. The images were converted into 3D casts and compared for accuracy against their laser scanned counterparts for surface area, volume, interpoint mismatches, and spatial overlap. Acceptability thresholds were set at <0.5 mm for interpoint mismatches and >0.70 for spatial overlap. ResultsThe test for smartphone precision in comparison with that of the laser scanner showed a difference in surface area of 774.22 ±295.27 mm2 (6.9% less area) and in volume of 4228.60 ±2276.89 mm3 (13.4% more volume). Both acceptability thresholds were also met. The test for accuracy among smartphones 1, 2, and the laser scanner showed no statistically significant differences (P>.05) in all 4 parameters among the groups while also meeting both acceptability thresholds. ConclusionsSmartphone cameras used to capture 48 overlapping gypsum cast ear images in a controlled environment generated 3D models parametrically similar to those produced by standard laser scanners.

Preoperative Flap Surgery Simulation for a Case of Cryptotia Using a 3D Printer

Summary:Cryptotia is a congenital auricular deformity. Common methods of surgical reconstruction involve creating an auricular temporal groove using local flaps and/or a skin graft. However, it can be difficult to determine which method is most suited to the unique 3-dimensional (3D) structure of each cryptotic ear. Here, we showed that creating 3D ear models of a cryptotic ear with a 3D camera and printer and using these models to simulate surgery with two different flap methods (cat’s-ear and square) allowed selection of a reconstruction method that led to good outcomes after the actual surgery. The patient was a 7-year-old girl with left cryptotia. A 3D camera was used to acquire 3D data for the ear. After structural analysis, a home 3D printer was used to print the data into 3D ear models using an elastic material. These models exhibited good plasticity. After subjecting the models to simulated cat’s-ear and square flap surgeries, the cat’s-ear flap method was considered to better reproduce the healthy side of the ear compared with the square flap method for this particular case. Ear morphology during and after the actual surgery closely resembled the model-ear morphology during and after the simulated cat’s-ear flap surgery. We successfully created a full-scale 3D model with good plasticity using a 3D camera and 3D printer. This allowed easy, noninvasive preoperative evaluation and identification of the most suitable operation for the specific case, facilitating easier, more successful surgery.

Microtia deformity correction with three-dimensional and two-dimensional templates: A pathway for effective ear reconstruction

Expression of anatomical landmarks of the ear results in the success of ear reconstruction in microtia. Ear reconstruction in India is routinely done with a two-dimensional (2D) template by tracing the contralateral ear with an X-ray sheet. There is a need of a proper template to understand the finer details of the ear reconstruction. In this report, a three-dimensional (3D) template in association with a 2D template is utilised to augment the reconstruction ability of the plastic surgeon. After computed tomography scan, an accurate 3D scan of the normal ear is obtained. Using the 3D ear model file, a negative mould of the ear 3D model is developed and fed into a 3D printer. The model is sterilised and used for reconstruction. Hyperbaric oxygen therapy is used as an adjunct for the procedure. Simultaneous utilisation of 3D and 2D templates for ear reconstruction appears to increase the ability of plastic surgeons to recreate the ear.

Augmenting a 3D morphable model of the human head with high resolution ears

We present a parts-based 3D Morphable Model (3DMM) of the full human head, with particular emphasis on modelling the complex shape of the ear as a flexible, high-resolution separate part. The 3D ear model part undergoes an iterative process of refinement that employs data augmentation using a 2D image dataset with landmarked ears. Evaluations using several performance metrics validate the training process and the resulting model. We make the new ear model and our reconstructed training dataset publicly available. We merge the trained high-resolution 3DMM of the ear with a publicly-available 3DMM of the full head that has a much lower resolution in the ear regions. The resulting parts-based 3DMM provides more shape variation and more shape detail in the ears, and we demonstrate a higher fidelity overall model fit to raw data.

Numerical modeling of percutaneous auricular vagus nerve stimulation: a realistic 3D model to evaluate sensitivity of neural activation to electrode position.

Percutaneous stimulation of the auricular branch of the vagus nerve (pVNS) by miniaturized needle electrodes in the auricle gained importance as a treatment for acute and chronic pain. The objective is to establish a realistic numerical model of pVNS and investigate the effects of stimulation waveform, electrodes' depth, and electrodes' position on nerve excitation threshold and the percentage of stimulated nerves. Simulations were performed with Sim4Life. An electrostatic solver and neural tissue models were combined for electromagnetic and neural simulation. The numerical model consisted of a realistic high-resolution model of a human ear, blood vessels, nerves, and three needle electrodes. A novel 3D ear model was established, including blood vessels and nerves. The electric field distribution was extracted and evaluated. Maximum sensitivity to needles' depth and displacement was evaluated to be 9.8 and 15.5% per 0.1mm, respectively. Stimulation was most effective using biphasic compared to mono-phasic pulses. The established model allows easy and quantitative evaluation of various stimulation setups, enabling optimization of pVNS in experimental settings. Results suggest a high sensitivity of pVNS to the electrodes' position and depth, implying the need for precise electrode positioning. Validation of the model needs to be performed.

3D Ear Normalization and Recognition Based on Local Surface Variation

Most existing ICP (Iterative Closet Point)-based 3D ear recognition approaches resort to the coarse-to-fine ICP algorithms to match 3D ear models. With such an approach, the gallery-probe pairs are coarsely aligned based on a few local feature points and then finely matched using the original ear point cloud. However, such an approach ignores the fact that not all the points in the coarsely segmented ear data make positive contributions to recognition. As such, the coarsely segmented ear data which contains a lot of redundant and noisy data could lead to a mismatch in the recognition scenario. Additionally, the fine ICP matching can easily trap in local minima without the constraint of local features. In this paper, an efficient and fully automatic 3D ear recognition system is proposed to address these issues. The system describes the 3D ear surface with a local feature—the Local Surface Variation (LSV), which is responsive to the concave and convex areas of the surface. Instead of being used to extract discrete key points, the LSV descriptor is utilized to eliminate redundancy flat non-ear data and get normalized and refined ear data. At the stage of recognition, only one-step modified iterative closest points using local surface variation (ICP-LSV) algorithm is proposed, which provides additional local feature information to the procedure of ear recognition to enhance both the matching accuracy and computational efficiency. On an Inter®Xeon®W3550, 3.07 GHz work station (DELL T3500, Beijing, China), the authors were able to extract features from a probe ear in 2.32 s match the ear with a gallery ear in 0.10 s using the method outlined in this paper. The proposed algorithm achieves rank-one recognition rate of 100% on the Chinese Academy of Sciences’ Institute of Automation 3D Face database (CASIA-3D FaceV1, CASIA, Beijing, China, 2004) and 98.55% with 2.3% equal error rate (EER) on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2, University of Notre Dame, South Bend, IN, USA, between 2003 and 2005).

A high-resolution head-related transfer function dataset and 3D ear model database

Binaural reproduction techniques, which use head-related transfer functions (HRTFs), become more and more popular. Human localization performance using binaural synthesis is, however, often poor due to non-individual HRTFs. To investigate the physical relationship between the ear geometry and the directional HRTFs, the ears and the HRTF datasets of 48 subjects are measured. The detailed ear models are obtained from magnetic resonance imaging scans of the subjects. Two rough scans series of the head and a detailed horizontal scan series, which covers both ears, are taken. The 3D model meshes, which are generated from the MRI scans, allow a very precise determination of anthropometric dimensions which can be used for the individualization of the HRTFs. Additionally, the relationship between the anthropometric dimensions and monaural cues of the HRTFs is studied by the boundary element method. These simulations show which parts of the pinna are involved in case of resonances or destructive interferences.

Fabrication of three-dimensional scan-to-print ear model for microtia reconstruction

BackgroundMicrotia is a congenital deformity of the external ear that occurs in 1 of every 5000 births. Microtia reconstruction using traditional two-dimensional templates does not provide highly detailed ear shapes. Here, we describe the feasibility of using a three-dimensional (3D) ear model as a reference. Materials and methodsSeven children aged from 11 to 16 (6 grade III and 1 grade II microtia) were recruited from Seoul National University Children's Hospital, Korea. We generated 3D–computer-aided design models of each patient's ear by performing 3D laser scanning for a mirror-transformed cast of their normal ear. The 3D-printed ear model was used in microtia reconstruction surgery following the Nagata technique, and its shape was compared with the casted ear model. ResultsOne patient experienced irritation caused by accidently pouring resin into the external auditory meatus, and another had minor skin necrosis; both complications were successfully treated. The average percentage differences of the superior, inferior, anterior, posterior, and lateral views between the casted and 3D-printed ear models were 1.17%, 1.48%, 1.64%, 1.80%, and 5.44%, respectively (average: 2.31%), where the difference between the casted ear models and traditional two-dimensional templates were 16.03% in average. ConclusionsOur results show that simple microtia reconstruction can be performed using 3D ear models. The 3D-printed ear models of each patient were consistent and accurately represented the thickness, depth, and height of the normal ear. The availability of the 3D-printed ear model in the operating room reduced the amount of unnecessary work during surgery.

An ear biometric system based on artificial bees and the scale invariant feature transform

Ear recognition is a new biometric technology that competes with well-known biometric modalities such as fingerprint, face and iris. However, this modality suffers from common image acquisition problems, such as change in illumination, poor contrast, noise and pose variation. Using a 3D ear models reduce rotation, scale variation and translation-related problems, but they are computationally expensive. This paper presents a new architecture of ear biometrics that aims at solving the acquisition problems of 2D ear images. The proposed system uses a new ear image contrast enhancement approach based on the gray-level mapping technique, and uses an artificial bee colony (ABC) algorithm as an optimizer. This technique permits getting better-contrasted 2D ear images. In the feature extraction stage, the scale invariant feature transform (SIFT) is used. For the matching phase, the Euclidean distance is adopted. The proposed approach was tested on three reference ear image databases: IIT Delhi, USTB 1 and USTB 2, and compared with traditional ear image contrast enhancement approaches, histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE). The obtained results show that the proposed approach outperforms traditional ear image contrast enhancement techniques, and increases the amount of detail in the ear image, and consequently improves the recognition rate.

Human Centric Recognition of 3D Ear Models

AbstractComparing ear photographs is considered to be an important aspect of disaster victim identification and other forensic and security applications. An interesting approach concerns the construction of 3D ear models by fitting the parameters of a ‘standard’ ear shape, in order to transform it into an optimal approximation of a 3D ear image. A feature list is then extracted from each 3D ear model and used in the recognition process. In this paper, we study how the quality and usability of a recognition process can be improved by computational intelligence techniques. More specifically, we study and illustrate how bipolar data modelling and aggregation techniques can be used for improving the representation and handling of data imperfections. A novel bipolar measure for computing the similarity between corresponding feature lists is proposed. This measure is based on the Minkowski distance, but explicitly deals with hesitation that is caused by bad image quality. Moreover, we investigate how forensic e...