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    THẠC SĨ 3D facial model analysis for clinical medicine

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  6. 3D facial model analysis for clinical medicine

    IV

    Table of contents

    Declaration . I
    Acknowledgments . II
    Table of contents . IV
    Abstract . VII
    List of Tables IX
    List of Figures . X
    Acronyms XIV
    Chapter 1. Introduction 1
    1.1 Facial paralysis and diagnosis 2
    1.1.1 Facial Paralysis 2
    1.1.2 Clinical Facial Paralysis Assessment Methods . 5
    1.1.3 2D Image and Video Based Computer Aided Dia gnosis . 9
    1.2 Facial highlight Features Analysis . 13 Table of contents
    V

    1.3 Objectives of the Thesis . 18
    1.4 Overview of the Thesis 19
    Chapter 2. Methodology 21
    2.1 3D Curvatures . 21
    2.2 Iterative Closest Point 30
    2.3 Artificial Neural Network . 32
    Chapter 3. Objective Grading System for Facial Paralysis Diagnosis . 41
    3.1 Overview . 41
    3.2 Data acquisition 43
    3.3 Objective Measurement of the surface contour . 47
    3.4 Asymmetry degree index 51
    3.5 Noise Injected Neural Network . 55
    3.6 Performance Evaluation 58
    3.7 Results 59
    3.8 Discussion and Conclusion . 65 Table of contents
    VI

    Chapter 4. Facial Highlight Features Analysis . 68
    4.1 Introduction 68
    4.2 Data Acquisition . 69
    4.3 Highlight region extraction 74
    4.4 Facial highlight features 77
    4.4.1 Highlight regions distribution 78
    4.4.2 Highlight of nasal bridge 78
    4.4.3 Schema of forehead highlight region. . 79
    4.5 3D Objective Measurement of the surface cont our . 83
    Chapter 5. Conclusion 86
    References 90


    VII


    Abstract
    This thesis aims to investigate both facial paralysis diagnosis and facial
    highlight features based on 2D and 3D facia l models.
    First, a novel automated objective asymmetry grading system is
    developed for facial paralysis diagnosis. The development of this grading
    system combines observations and clinical ass essments of the patients for
    different degrees of motion dysfunction in v arious facial expressions. To
    improve the performance of the system, highe r order surface properties in
    facial imaging technique for 3D model analysis are used. Also, to
    overcome the subjectivity of diagnosis encountered by the landmark
    based computer aided grading methods, facial symmetry grading is
    carried out based on fine registration result of the original and mirror
    facial mesh by the iterated closest‐point algorithm (ICP), which does not
    rely on any landmarks. Moreover, to avoid overfitting caused by small
    sample set, the noise injected artificial ne ural networks (ANNs) in feature
    extraction and classification for 3D objects were implemented. Compared
    with standard ANNs, the accuracy, sensitivity and specificity of the Abstract
    VIII

    proposed noise‐injected ANNs are significant ly improved. The system is
    also tested with data of patients having fo llow‐up treatment and diagnosis
    after the initial treatment. The proposed ANN system can detect the
    improvement of the patients quite well. A plausible explanation of the
    appreciably improved performance is that the injected noise increases the
    generalization ability, and reduces the sensi tivity to the disturbance in this
    manner.
    Meanwhile, the highlight feature patterns of natural faces are explored as
    a planning aid for plastic surgery. Different from previous reported
    studies on attractive face patterns, which h ave mainly based their criteria
    on facial profile, this study intends to de termine the position and shape of
    the highlights of natural faces across race and gender. Some relevant
    conclusions can be drawn from the present s tudy. First, nasal highlights
    are discontinuous, thus the implant or fille r should keep the dorsum and
    tip at different levels. Second, the shape of the nasion saddle is intimately
    associated with race. Also, the forehead hig hlight has mainly two types, T
    shape and maple leaf shape. The distributions of these two types are
    closely related to race and gender.

    IX


    List of Tables
    Table 2.1 Surface shapes and their corresponding principal, mean and
    Gaussian curvatures, and the Shape index 53 . . 26
    Table 3.1 Threshold value chosen for no‐ma tch points filter. . 55
    Table 3.2 Results provided by the ANNs with input of {RD, RGC} in the
    conventional manner and with noise‐injected methods. . 62
    Table 3.3 Results provided by the ANNs with input of {RD, RSI} in the
    conventional manner and with noise‐injected methods. . 63
    Table 3.4 Diagnosis results comparison for the patients before and after
    medical treatments . 64
    Table 4.1 Age, race and gender information of sample subjects . 70
    Table 4.2 Race and gender distributions of the highlight shape on the nasal
    bridge. 79
    Table 4.3 Race and gender distributions of the forehead highlight shape
    for the 64 subjects. 81 List of Figures
    X


    List of Figures
    Figure 1.1 Patients with Bell’s palsy. 7 (a) Asymmetric elevation of brow
    and wrinkling of the forehead; (b) Incomplet e eyelid closure; (c) Flattened
    nasolabial fold and poor turning upward of the left side of mouth. 3
    Figure 1.2 Anatomy of the facial nerve. 9 . 4
    Figure 1.3 SFGS standard form. . 9
    Figure 1.4 Comparison of two pictures with Andie MacDowell in different
    ages. 13
    Figure 1.5 Study of the proportions of huma n body and head by Leonardo
    da Vinci. . 16
    Figure 1.6 Makeup expert applies highlight f oundation on the face of the
    model, and tries to enhance the facial feat ures. 46 17
    Figure 1.7 Overview of the objective asymmet ry grading system . 19
    Figure 2.1 Normal planes in directions of p rincipal curvatures of a saddle
    surface. 51 22 List of Figures
    XI

    Figure 2.2 The Shape index as a shape descriptor for different shape of
    surface 53 . 25
    Figure 2.3 Architecture of Artificial neural network 33
    Figure 2.4 Model of a neuron k. . 34
    Figure 2.5 Architectural graph of multilayer perceptron feedforward
    networks. . 36
    Figure 2.6 Overfitting occurs when excessive number of nodes is used in
    the MLP neural network. 39
    Figure 3.1 (a) 3dMDface system and (b) reco nstructed 3D image. . 44
    Figure 3.2 Detail of triangulated polygon fa cial mesh. 44
    Figure 3.3 3D models of face acquired by 3 dMD system for four different
    expressions: (a) straight and natural stare, (b) smiling to show teeth, (c)
    raising eyebrow to wrinkle forehead, and (d) closing the eyes tightly. . 46
    Figure 3.4 Rendering of (a) Gaussian curvatu re and (b) Shape Index color
    map on 3D face scan model of smiling to s how teeth expressions. 50
    Figure 3.5 Registration between original and mirror faces by ICP. 80 . 52 List of Figures
    XII

    Figure 3.6 (a) Original mesh. (b) Mirror me sh. (c) ICP registration result of
    original and mirror meshes. . 52
    Figure 3.7 Color maps of the difference bet ween the original and mirror
    meshes. (a) Geometry Distance, (b) difference of the Gaussian curvature
    and (c) difference of the Shape Index. . 54
    Figure 4.1 Anatomy of human face. 68
    Figure 4.2 Anterior and lateral facial views of six sample subjects. Rows
    correspond to six subjects of (a) Chinese male, (b) Chinese female, (c)
    Eurasian male, (d) Eurasian female, (e) Cauc asian male, and (f) Caucasian
    female. Columns correspond to different views of (1) anterior view, and (2)
    lateral view. . 72
    Figure 4.3 (a) Plaster cast of nose region; (b) 3D model reconstructed by
    scanning the plaster cast. 73
    Figure 4.4 Grayscale image with nose tip la ndmark prn and alar landmark
    al added. 74
    Figure 4.5 Facial highlight region extraction process. Rows correspond to
    six subjects of (a) Chinese male, (b) Chine se female, (c) Eurasian male, (d)
    Eurasian female, (e) Caucasian male, and (f) Caucasian female. Columns List of Figures
    XIII

    correspond to highlight extraction steps of (1) grayscale image, (2) setting
    gray level threshold for grayscale image, and (3) extracted highlight
    regions. 76
    Figure 4.6 Two type of forehead highlight regions: (a) T shape, and (b)
    Maple leaf shape. . 80
    Figure 4.7 Gaussian curvature value color ma p 84


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