28 research outputs found
μΉλ£λμ§ μμ μΉμ£ΌμΌμ΄ μΉκ³Ό μνλνΈμ 골μ μ°©μ λ―ΈμΉλ μν₯
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Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : μΉμκ³Όνκ³Ό, 2017. 2. ꡬμ.Background: There have been previous studies on the relationship between periodontitis and periimplantitis, but limited information is available on how periodontitis affects osseointegration and wound healing of newly placed dental implants adjacent to natural teeth. The objective of the present experiment was to evaluate healing around dental implants adjacent to teeth with untreated experimental periodontitis.
Methods: The experiment included 6 male beagle dogs. Scaling and plaque control procedures were performed in 3 dogs (the control group). In the other 3 dogs (the experimental group), retraction cords and ligature wires were placed subgingivally around all premolars and the first molars. Induced experimental periodontitis was confirmed after 3 months. Each control or experimental group was divided into 2 subgroups depending on the timing of implant placement (immediate/delayed). Twelve dental implants (2 implants for each dog) were placed immediately and the other 12 dental implants (2 implants for each dog) were placed two months after extraction. The animals were sacrificed 2 months after implant placement. Histological and histometric analysis were performed.
Results: Four implants (3 from immediate and 1 from delayed placement) failed in the experimental group. There were significant differences in the percentage of bone-to-implant contact and marginal bone volume density between the control and the experimental groups.
Both parameters were significantly lower in the experimental group than in the control group (P<0.05). There was a tendency toward more marginal bone loss in the experimental group than the control group.
Conclusion: Immediately placed implants have a higher failure rate than delayed placed implants. Untreated experimental periodontitis was correlated with a compromised osseointegration in delayed placed implants.INTRODUCTION 1
MATERIALS & METHODS 5
RESULTS 11
DISCUSSION 14
References 25
κ΅λ¬Έμ΄λ‘ 32Docto
μμ ν©λΏλ¦Όμ κΈ°λ°ν κ°λ³μ체μΈμ¦
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2018. 8. μ‘°λ¨μ΅.Nowadays biometrics systems for identification or authentication of a person are everywhere. These system have a number of advantages. In particular, biometrics traits cannot be lost or forgotten compared to passwords. Moreover biometric identification offers good accuracy. However, their uses raises several privacy concerns, especially in their storage. In fact, if a password is stolen, it can be replaced by a new password. This is not possible in biometrics. To overcome the security problems, biometric cryptosystems (BCS) and cancelable biometrics (CB) represent emerging technologies of biometrics template protection addressing this concerns and improving public confidence and acceptance of biometrics. BCS are designed to securely bind a digital key to a biometric or generate a digital key from a biometric offering solutions to biometric-dependent key-release and biometric template protection while CB consist of intentional, repeatable distortions of biometric signals based on transforms which provide a comparison templates in the transformed domain.
In this dissertation, a cancelable biometric scheme for iris recognition system is proposed. The first proposed CB method uses the reduced random permutation and binary salting (RRP-BS). RRP-BS consists of random permutation of binary iris template followed by the orthogonal binary salting. The random permutation perturbs the rows of iris template structure and eliminates some rows of iris template. This guarantees the non-invertibility of CB scheme even though the all of bio-security keys is stolen. Then this CB scheme also proposes an orthogonal binary salting method, where the random binary keys are generated by Gram-Schmidt orthogonalization. The orthogonality of random keys maximizes the Hamming distances among binary-salted templates. Thus, the inter classes (different users) are discriminated while the intra class (one user) is well identified. While this method has good performance and unlinkability, its non-invertibility is vulnerable to muliplicity or hill-climbing attacks. The second proposed method uses more robust non-invertibility transform based on the first method. We use the RRP-BS as the biometric salting, and use the Hadamard product for enhancing the non-invertibility of salted data. Moreover, to overcome the shortcomings of perserving the keys of the conventional salting methods, we generate several templates for an input, and define non-coherent and coherent matching regions among these templates. We show that salting the non-coherent matching regions is less influential on the overall performance. Specifically, embedding the noise in this region does not affect the performance, while making the data difficult to be inverted to the original. For the evaluation, we use three datasets, namely CASIA V3 iris-interval, IIT Delhi iris, and ND-Iris-0405. The extensive evaluations show that the proposed algorithm yields low error rates and good intra/inter classification performances, which is better or comparable to the existing methods. Moreover, the security analysis ensures that the proposed algorithm satisfies non-invertibility and unlinkability, and is robust against several attacks as well.1 INTRODUCTION 1
1.1 Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Outline of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . 4
2 BACKGROUND 6
2.1 Iris Biometric Processing . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Potential Attacks against Cancelable Biometrics . . . . . . . . . . . . 9
3 NON-INVERTIBLE CANCELABLE IRIS BIOMETRICS USING RAN- DOM PERMUTATION AND ORTHOGONAL KEYS 10
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3 Proposed Non-invertible Binary Salting . . . . . . . . . . . . . . . . 15
3.3.1 Binary Salting Review . . . . . . . . . . . . . . . . . . . . . 15
3.3.2 Random permutation . . . . . . . . . . . . . . . . . . . . . . 16
3.3.3 Orthogonal random key . . . . . . . . . . . . . . . . . . . . 17
3.3.4 Cancelable Iris Biometric System . . . . . . . . . . . . . . . 18
3.3.5 Analysis of stolen key situations . . . . . . . . . . . . . . . . 18
3.4 Experiments and Discussion . . . . . . . . . . . . . . . . . . . . . . 21
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4 CANCELABLE IRIS BIOMETRICS USING NOISE EMBEDDING 32
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2.1 Non-Invertible Transform Approaches . . . . . . . . . . . . . 36
4.2.2 Biometric Salting Approaches . . . . . . . . . . . . . . . . . 37
4.3 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.3.1 Binary Salting . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.3.2 Reduced Random Permutation . . . . . . . . . . . . . . . . . 41
4.4 Proposed CIB System . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.4.1 Template Creation . . . . . . . . . . . . . . . . . . . . . . . 42
4.4.2 Reference Template Selection . . . . . . . . . . . . . . . . . 48
4.4.3 Finding Coherent and Non-Coherent Matching Region . . . . 48
4.4.4 Noise Embedding . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4.5 Modifications for Alignment . . . . . . . . . . . . . . . . . . 50
4.4.6 Authentication . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.4.7 Differences with IFO hashing . . . . . . . . . . . . . . . . . 52
4.5 Experiments and Discussion . . . . . . . . . . . . . . . . . . . . . . 53
4.5.1 Experimental Databases . . . . . . . . . . . . . . . . . . . . 53
4.5.2 Scores for evaluation . . . . . . . . . . . . . . . . . . . . . . 54
4.5.3 Effect of parameters . . . . . . . . . . . . . . . . . . . . . . 56
4.5.4 Comparison with other algorithms . . . . . . . . . . . . . . . 58
4.5.5 Unlinkability . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.6 Security Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5 CONCLUSION 74
Bibliography 76
Abstract (In Korean) 81Docto
Research on cardiovascular ultrasound data acquisition and analysis for diagnosing coronary artery stenosis
Doctorκ΄μλλ§₯ νμ°©μ¦μΌλ‘ μΈν νλ₯μ λλ₯λ μ£Όλ³ κ΄μλλ§₯ μ‘°μ§μ μν₯μ λ―ΈμΉλ©° μΈμ ν μ¬κ·Ό μ‘°μ§μ μ€λμ€ μ£Όνμ μ§λμ λ°μμν¨λ€. κ΄μ λλ§₯ λνλ¬ μ§λ μΈ‘μ λ² (CDV)μ μ΄μνλ₯Ό μ¬μ©νμ¬ κ΄μ λλ§₯ νμ°©μ¦μΌλ‘ λ°μν μ¬κ·Όμ μ§λμ κ°μ§νλ λΉμΉ¨μ΅μ μ§λ¨κΈ°μ μ΄λ€. μ΅κ·Ό μ§νλ CDV κΈ°μ μ°κ΅¬λ κ΄μλλ§₯ μ§ν μ§λ¨μμ μλΉν λ―Όκ°λ λ° νΉμ΄λλ₯Ό 보μ¬μ£Όμ§λ§, κΈ΄ μ§λ¨μκ°κ³Ό, μ μκ΅° νΌνμμκ²μλ κ³ μ£Όν μ§λ μ±λΆμ΄ κ²μΆλλ λ¬Έμ λ±μ΄ μ¬μ ν λ¨μμλ€. μ΄λ¬ν λ¬Έμ λ₯Ό 극볡νκΈ° μν΄ μ°λ¦¬λ μλμ κ°μ΄ 3 κ°μ§ λ°μ΄ν° μμ§ λ°©λ²μ μ μνλ€. λν, μμ μ°κ΅¬λ₯Ό ν΅ν΄ μ μ λ μ κ·Ό λ°©μμ κ°λ₯μ±μ κ²μ¦νλ€.
Interleaved λ°μ΄ν° μμ§ λ°©λ²μμλ, λ κ³³μ λ€λ₯Έ μμΉλ‘λΆν° μ΄μν νμ€λ₯Ό κ΅μ°¨μ μΌλ‘ μ‘μμ νμ¬ λ°μ΄ν°λ₯Ό νλν ν, κ³΅ν΅ μ νΈ μ±λΆμ μ κ±°νμ¬ νμ°©μ μν μ§λ μ±λΆλ§ κ²μΆνλ κ²μ λͺ©νλ‘ νλ€. μ΄ λ°©μμ λ°μ΄ν° νλμ ν΅νμ¬ μ¬λ¬ μμμμ 곡ν΅μ μΌλ‘ λνλ μ μλ κ°μΈμ κ³ μ ν κ³ μ£Όν μ§λ νΉμ±μ ν¨κ³Όμ μΌλ‘ κ²μΆν μ μλ€. λν νΉμ μκ°μ λ°μνλ λ
Έμ΄μ¦ μ±λΆμ ν¨κ³Όμ μΌλ‘ νν°λ§ ν μ μλ€. Interleaved λ°μ΄ν° μμ§ λ°©λ²μ μ΄μ©νμ¬ μμ μ°κ΅¬μμ μ΄ 11 λͺ
μ νΌνμλ₯Ό λͺ¨μ§νλ€. RCA κ΄μλλ§₯μ κ²½μ° 83 %μ λ―Όκ°λμ 80 %μ νΉμ΄λλ₯Ό μ»μμΌλ©° LAD κ΄μλλ§₯μ κ²½μ° 100 %μ λ―Όκ°λμ 83 %μ νΉμ΄λλ₯Ό μ»μλ€.
Arrayed-range gate λ°μ΄ν° μμ§μμλ νμ°©μ μν κ³ μ£Όν μ§λμ΄ μ¬κ·Ό μ‘°μ§μ μ΄λ»κ² λΆν¬νλ μ§ λΆμνλ κ²μ λͺ©νλ‘ νμλ€. μ°λ¦¬μ λͺ©νλ μ¬κ·Ό μ‘°μ§ λ΄μ νμ°©μ΄ μλ κ²½μ°μ μλ κ²½μ° μ§λ λΆν¬μ κ²½ν₯μ±μ λΉκ΅νμ¬ κ΄μ°°νλ κ²μ΄λ€. κΈ°μ‘΄μ λ°©μμμ λ°νμ§ λͺ»ν νμ°©μ μν μ§λμ μ ν νΉμ±μ λΆμνμλ€. λν νμ°©μ΄ μ‘΄μ¬νλ κ²½μ°μ κ·Έλ μ§ μμ κ²½μ°μ μ¬κ·Όμ μμ§μμμ μ μλ―Έν μ°¨μ΄λ₯Ό νμΈνμλ€. μ΄ λ°©μμ μ΄μ©ν μμμ°κ΅¬μμ μ΄ 33 λͺ
μ νΌνμλ₯Ό λͺ¨μ§νλ€. μ°λ¦¬μ μ§λ¨ μκ³ λ¦¬μ¦μ μ μκ΅°κ³Ό νμκ΅°μ 75 %μ λ―Όκ°λμ 83 %μ νΉμ΄λλ‘ ν¨κ³Όμ μΌλ‘ λΆλ₯νλ€.
Wide range gate (WRG) λ°μ΄ν° μμ§μμλ λ°μ΄ν° νλ λμμ μ΄μν νμ€λ₯Ό μ§μ€νλ λ°©μμ μ΄μ©νλ€. μ€μκ°μΌλ‘ λ°μ΄ν° νλ λμμ μμμ μ
λ ₯λ°μ ν, ν΄λΉ μμμΌλ‘λΆν° κ²μΆλλ μ νΈκ° μ΅λκ° λλλ‘ νλ©΄ν νμ€λ₯Ό λ°μμμΌ WRG λ°μ΄ν° νλμ μ§ννλ€. μ΄ λ°©μμ ν¬μ»€μ€λ κΈ°λ° λ°©μλ€μ΄ κ°μ§κ³ μλ λ§€μ° κΈ΄ μ§λ¨μκ°κ³Ό νΉμ λΆλΆμμλ§ λμ μ νΈ λ μ‘μλΉμ λ¬Έμ μ λ€μ ν΄κ²°ν μ μμλ€. λμ μμμμ λ°μ΄ν°λ₯Ό νλνκΈ° λλ¬Έμ μ 보μ²λ¦¬μ λ§€μ° λ§μ μμ λ°μ΄ν°λ₯Ό νμ©ν μ μκ³ , μ΄μ΄ν μμμμ λ°μ΄ν° λΆμμ΄ κ°λ₯νκΈ° λλ¬Έμ κ°κ°μΈλ§λ€ λ€λ₯Έ κ΄μλλ§₯μ νΉμ± νΉμ νμ°©μ μν μ§λ μ ν νΉμ±μ κ³ λ €ν μ§λ κ²μΆ μκ³ λ¦¬μ¦μ κ°λ°ν μ μλ€. μ¨μ ν μ§λ¨ μκ³ λ¦¬μ¦κ³Ό νλ‘ν μ½μ μ μνμ¬ μ€μ λ‘ λμμκ° λ³μμ λ°©λ¬Ένμμ κ²½μ°, μ§λ¨ κ²°κ³Όλ₯Ό λμΆν μ μμλ€. μμ μ°κ΅¬μμ μ΄ 100 λͺ
μ νΌνμλ₯Ό λͺ¨μ§νλ€. κ΄μ λλ§₯ μ‘°μμ κ²°κ³Όμ λΉκ΅νμ¬ WRG λ°μ΄ν° λΆμμ λ―Όκ°λμ νΉμ΄λλ κ°κ° 80 %μ 84 %μλ€. λν λ€λ³λ λΆμμμ, WRG λ°μ΄λΈλ‘λ©νΈλ¦¬ μ§λ¨ κ²°κ³Όλ CADμ λν λ
립μ μΈ μμΈ‘ μΈμμλ€.
μ°λ¦¬λ μμ κ°μ΄ μ΄μνλ₯Ό μ¬μ©νμ¬ CADλ₯Ό κ°μ§νλ μλ‘μ΄ μ§λ¨ λ°©λ²λ€μ μ μνμλ€. μλ‘μ΄ λ°μ΄ν° μμ§ λ°©λ²μ μλΉν λ―Όκ°λμ νΉμ΄λλ₯Ό 보μ΄λ©°, CADμ μ΄κΈ° μ§λ¨ λκ΅¬λ‘ ν° μ μ¬λ ₯μ 보μ¬μ£Όμλ€.The turbulence of blood flow caused by coronary artery stenosis has an impact on the surrounding coronary artery tissue, and creates audio-frequency vibrations to the adjacent myocardial wall. Coronary Doppler vibrometry (CDV) is a non-invasive diagnosis to detect the vibrations from stenosis in coronary artery using ultrasound. Although recent CDV shows considerable sensitivity and specificity in diagnosing coronary artery disease, there still remains a serious problem that normal subjects can also have high-frequency components in vibration. To overcome these problems, we proposed three data acquisition methods. Also, we confirmed the feasibility of the proposed approach through a clinical study.
In interleaved data acquisition, we transmit pulse to two different positions in an interleaved manner and obtain differential information of two reflected signals in order to eliminate common noise thereby enhancing the quality of stenosis-induced vibration. We recruited total 11 subjects in a clinical study. We obtained a sensitivity of 83% and a specificity of 80% in the right coronary artery cases and obtained a sensitivity of 100% and a specificity of 83% in the left anterior descending cases.
In arrayed-range-gate data acquisition, we developed new data acquisition and analysis algorithms that focus on the spatial distribution of high-frequency vibration in the myocardial tissue. Our purpose is to observe the location-dependent variation of vibration in the myocardial tissue. We recruited total 33 subjects in a clinical study. Our proposed algorithm effectively classifies normal subjects and patients with sensitivity of 75% and specificity of 83%.
In wide range gate (WRG) data acquisition, we investigated the diagnostic feasibility of a novel diagnostic method using wide range gate ultrasound data acquisition for diagnosing coronary artery disease (CAD). The WRG data acquisition detects high-frequency vibrations from coronary artery stenosis, using pulse-wave Doppler ultrasound. We recruited total 100 subjects in a clinical study. As compared with the results of coronary angiography, the sensitivity and specificity of the WRG data analysis were 80% and 84%, respectively. In a multivariate analysis, a positive vibrometry result was an independent predictive factor for CAD.
We proposed a new diagnostic method for detecting CAD using ultrasound. The new data acquisition method showed good potential as an initial diagnostic tool for CAD
(A)Clinical study of alveolar bone quality using fractal dimension and implant stability quotient
Thesis(master`s)--μμΈλνκ΅ λνμ :μΉμνκ³Ό μΉμ£Όκ³Όνμ 곡,2006.Maste