A Novel Algorithm for Forensic Identification Using Geometric Cranial Patterns in Digital Lateral Cephalometric Radiographs in Forensic Dentistry
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Proposed K-VIN Algorithm
2.2. Radiographic Landmark Selection for Encoding Cranial AM Structural Patterns
2.3. Geometric Measurement of Craniofacial Bone Structures
2.4. Ratio Calculation
2.5. Encoding
- Encoder_1 = Code (RD) + Code (RD) + Code (RD × RAg) + Code (RD × RAr).
- Encoder_2 = Code (RAg) + Code (RAg) + Code (RAg × RD) + Code (Rag × RAr).
- Encoder_3 = Code (RAr) + Code (RAr) + Code (RAr × RD) + Code (RAr × RAg).
- Combination (string) = Encoder_1 + Encoder_2 + Encoder_3.
- Regularizer 1: This regularizer addresses the algorithm’s sensitivity to less stable cranial structures, such as the mandibular position (lower jaw). The position of the mandible can change over time due to factors like tooth loss, decay, and minor growth during adulthood [46], which can affect its reliability for identity recognition and reduce accuracy. To mitigate this issue, the regularizer assigns greater weight to measurements of more stable structures, such as the maxilla and cranial base, and less weight to the mandible (Figure 2).
- Regularizer 2: This regularizer defines interval values for encoding, with options for 2%, 5%, or 10% intervals. For example, values can be encoded in 2%, 5%, or 10% intervals, where values from 0 to 0.02 or 0 to 0.05 are assigned a specific code (e.g., code A). The same applies to the 10% interval. By default, a 5% interval is used, balancing between hypersensitivity (2%) and hyposensitivity (10%) in the encoding process.
2.6. Storage of Encoded Cephalogram Data
2.7. Victim Identification Stage
2.8. Algorithm Testing (Experimental Setup)
3. Results
4. Discussion
5. Conclusions
6. Patents
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Landmark | Description |
---|---|
Nasion (NA) | The anterior point where the nasal and frontal bones intersect [42,43]. |
Sella (S) | The midpoint of the pituitary fossa, also known as the sella turcica [42,43]. |
Orbitale (Or) | The lowest point on the inferior margin of the orbit [42,43]. |
ANS | Anterior nasal spine, the tip of the anterior nasal spine (sometimes modified as the point on the upper or lower contour of the spine where it is 3 mm thick) [42,43]. |
PNS | The posterior nasal spine, defined as the tip of the palatine bone’s posterior spine at the junction between the hard and soft palates [42,43]. |
Articulare (Ar) | The point where the contour of the posterior surface of the mandibular condyle intersects with the temporal bone [42,43]. |
Gonion (Go) |
|
Menton (Me) | The most inferior point on the chin [42,43]. |
Parameter | Definition | Formula |
---|---|---|
Distance (D) | The Euclidean distance between each pair of landmarks, denoted as p1 and p2, respectively.
| D = |
Angle ( | The angle between every set of three landmarks (p1, p2, and p3) with coordinates (x1, y1), (x2, y2), and (x3, y3) can be calculated by the following: First, vectors A and B are defined; then, their lengths are computed, followed by calculating their dot product, and finally, the angle ( between the three landmarks is obtained.
| |
Area (Ar) | The area of each triangle by each set of three landmarks can be determined using Heron’s formula [45]. To calculate the area of a triangle given the lengths of its sides (a, b, and c), first, the semi-perimeter (s) is defined; then, Heron’s formula is used to determine the area.
|
Parameter | Definition | Formula | Output (List of Values) |
---|---|---|---|
RD | The ratio of each line segment’s length to another was calculated and constrained between 0 and 1 by always dividing the smaller length by the larger length. | RD = {a1, a2, …, an} 0 < a <= 1 | |
RAg | The ratio of each angle to another angle was calculated to obtain this index, ensuring values between 0 and 1 by always dividing the smaller angle by the larger angle. | RAg = {b1, b2, …, bn} 0 < b <= 1 | |
RAr | The ratio of each triangle’s area to another triangle’s area was computed, ensuring values between 0 and 1 by always dividing the smaller area by the larger area. | RAr = {c1, c2, …, cn} 0 < c <= 1 |
Value | Code |
---|---|
0–0.1 | A |
0.1–0.2 | B |
0.2–0.3 | C |
0.3–0.4 | D |
0.4–0.5 | E |
0.5–0.6 | F |
0.6–0.7 | G |
0.7–0.8 | H |
0.8–0.9 | I |
0.9–1 | J |
Parameter | Definition | Result |
---|---|---|
Age (years) | Mean age ± standard deviation (SD) of samples | 22.21 ± 4.5 |
Similarity | Mean similarity ± SD | 91.02 ± 2.6% |
Similarity_AER | Mean similarity ± SD after applying the AER function | 98.10 ± 3.37% |
Accuracy | (TP + TN)/(TP + TN + FP + FN) | 0.975 |
Sensitivity | TP/(TP + FN) | 0.977 |
Specificity | TN/(TN + FP) | 0.952 |
Na | S | Or | Ar | ANS | PNS | Go1 | Go2 | Me | |
---|---|---|---|---|---|---|---|---|---|
1 | 4.233824 | 4.241515 | 3.035855 | 1.607974 | 2.95384 | 1.104472 | 4.896409 | 0.8858 | 4.854231 |
2 | 3.041754 | 4.631722 | 2.73602 | 3.530173 | 3.316864 | 1.459843 | 3.550859 | 0.966853 | 4.613667 |
3 | 3.279826 | 4.121985 | 2.573237 | 1.756341 | 4.302755 | 0.0854 | 4.44715 | 2.540002 | 4.421004 |
4 | 3.685795 | 4.362348 | 2.158848 | 1.821404 | 4.142899 | 2.22766 | 3.914375 | 1.915114 | 3.360346 |
5 | 3.045192 | 3.952148 | 2.215952 | 2.259347 | 3.713335 | 2.177729 | 4.085907 | 1.808773 | 3.816824 |
6 | 2.013631 | 4.297177 | 3.405217 | 0.985085 | 3.595169 | 1.449267 | 4.303165 | 1.838328 | 5.654552 |
7 | 4.738587 | 4.166425 | 3.456717 | 1.496716 | 4.764213 | 0.534224 | 4.581135 | 1.980466 | 4.912953 |
8 | 2.89302 | 4.155614 | 2.137153 | 3.506202 | 4.763274 | 0.514616 | 3.138656 | 2.695381 | 4.352564 |
9 | 1.551962 | 3.596292 | 2.295773 | 2.629039 | 2.685305 | 2.10584 | 4.133792 | 2.322259 | 3.718152 |
10 | 3.614392 | 3.609049 | 4.107028 | 3.739961 | 3.343799 | 0.92952 | 5.922959 | 2.687414 | 3.890276 |
11 | 2.1054 | 3.300165 | 3.393195 | 1.387695 | 3.034395 | 1.733976 | 4.372208 | 1.746393 | 3.356147 |
12 | 1.913625 | 4.078769 | 2.612472 | 2.188649 | 3.986002 | 1.062368 | 2.900914 | 1.117798 | 4.692608 |
13 | 2.268908 | 3.204426 | 4.185395 | 2.02836 | 3.406368 | 1.14404 | 4.631237 | 1.294914 | 3.921766 |
14 | 3.17777 | 3.490724 | 3.138276 | 1.924587 | 3.488155 | 2.142705 | 3.980733 | 2.246511 | 4.373506 |
15 | 3.074464 | 4.85546 | 3.241534 | 3.69165 | 3.600555 | 1.556844 | 3.824773 | 1.272621 | 4.184124 |
16 | 3.478464 | 2.838455 | 1.829814 | 2.50061 | 3.110236 | 0.745751 | 4.058575 | 2.336319 | 5.099332 |
17 | 2.835942 | 4.613818 | 3.104121 | 2.795506 | 2.58939 | 0.645531 | 3.949099 | 1.552377 | 3.230014 |
18 | 4.883149 | 5.029736 | 3.564132 | 0.380446 | 3.33089 | 3.066221 | 2.801093 | 3.5481 | 4.848982 |
19 | 2.494161 | 5.070583 | 2.616539 | 2.177864 | 2.492837 | 1.143635 | 4.83874 | 2.328205 | 5.04226 |
20 | 2.979627 | 3.883471 | 2.772158 | 2.207089 | 4.396735 | 1.729498 | 3.392602 | 2.778084 | 5.392706 |
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Kavousinejad, S.; Yazdanian, M.; Kanafi, M.M.; Tahmasebi, E. A Novel Algorithm for Forensic Identification Using Geometric Cranial Patterns in Digital Lateral Cephalometric Radiographs in Forensic Dentistry. Diagnostics 2024, 14, 1840. https://doi.org/10.3390/diagnostics14171840
Kavousinejad S, Yazdanian M, Kanafi MM, Tahmasebi E. A Novel Algorithm for Forensic Identification Using Geometric Cranial Patterns in Digital Lateral Cephalometric Radiographs in Forensic Dentistry. Diagnostics. 2024; 14(17):1840. https://doi.org/10.3390/diagnostics14171840
Chicago/Turabian StyleKavousinejad, Shahab, Mohsen Yazdanian, Mohammad Mahboob Kanafi, and Elahe Tahmasebi. 2024. "A Novel Algorithm for Forensic Identification Using Geometric Cranial Patterns in Digital Lateral Cephalometric Radiographs in Forensic Dentistry" Diagnostics 14, no. 17: 1840. https://doi.org/10.3390/diagnostics14171840