3D Shape Descriptor-Based Facial Landmark Detection: A Machine Learning Approach

Facial landmark detection on 3D human faces has had numerous applications in the literature such as establishing point-to-point correspondence between 3D face models which is itself a key step for a wide range of applications like 3D face detection and authentication, matching, reconstruction, and retrieval, to name a few. Two groups of approaches, namely knowledge-driven and data-driven approaches, have been employed for facial landmarking in the literature. Knowledge-driven techniques are the traditional approaches that have been widely used to locate landmarks on human faces. In these approaches, a user with sucient knowledge and experience usually denes features to be extracted as the landmarks. Data-driven techniques, on the other hand, take advantage of machine learning algorithms to detect prominent features on 3D face models. Besides the key advantages, each category of these techniques has limitations that prevent it from generating the most reliable results. In this work we propose to combine the strengths of the two approaches to detect facial landmarks in a more ecient and precise way. The suggested approach consists of two phases. First, some salient features of the faces are extracted using expert systems. Afterwards, these points are used as the initial control points in the well-known Thin Plate Spline (TPS) technique to deform the input face towards a reference face model. Second, by exploring and utilizing multiple machine learning algorithms another group of landmarks are extracted. The data-driven landmark detection step is performed in a supervised manner providing an information-rich set of training data in which a set of local descriptors are computed and used to train the algorithm. We then, use the detected landmarks for establishing point-to-point correspondence between the 3D human faces mainly using an improved version of Iterative Closest Point (ICP) algorithms. Furthermore, we propose to use the detected landmarks for 3D face matching applications

Pericentric Inversion of Chromosome 9 in an Infant With Ambiguous Genitalia

Pericentric inversion of Chromosome 9 is one of the most common chromosomal abnormalities, which could be associated with various manifestations in some cases. Herein, a patient is presented with ambiguous genitalia that karyotyping revealed pericentric inversion of Chromosome 9 (p12,q13). Pericentric inversion of Chromosome 9 could be considered in the list of differential diagnosis of those with ambiguous genitalia, while chromosomal karyotype and culture could be recommended in children with ambiguous genitalia

Mesh Generation and Flexible Shape Comparisons for Bio-Molecules

Novel approaches for generating and comparing flexible (non-rigid) molecular surface meshes are developed. The mesh-generating method is fast and memory-efficient. The resulting meshes are smooth and accurate, and possess high mesh quality. An isometric-invariant shape descriptor based on the Laplace- Beltrami operator is then explored for mesh comparing. The new shape descriptor is more powerful in discriminating different surface shapes but rely only on a small set of signature values. The shape descriptor is applied to shape comparison between molecules with deformed structures. The proposed methods are implemented into a program that can be used as a stand-alone software tool or as a plug-in to other existing molecular modeling tools. Particularly, the code is encapsulated into a software toolkit with a user-friendly graphical interface developed by the authors

An Application of Manifold Learning in Global Shape Descriptors

With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to planets. Appropriate shape descriptors are critical for accurate (and efficient) shape retrieval and 3D model classification. Several spectral-based shape descriptors have been introduced by solving various physical equations over a 3D surface model. In this paper, for the first time, we incorporate a specific manifold learning technique, introduced in statistics and machine learning, to develop a global, spectral-based shape descriptor in the computer graphics domain. The proposed descriptor utilizes the Laplacian Eigenmap technique in which the Laplacian eigenvalue problem is discretized using an exponential weighting scheme. As a result, our descriptor eliminates the limitations tied to the existing spectral descriptors, namely dependency on triangular mesh representation and high intra-class quality of 3D models. We also present a straightforward normalization method to obtain a scale-invariant and noise-resistant descriptor. The extensive experiments performed in this study using two standard 3D shape benchmarks—high-resolution TOSCA and McGill datasets—demonstrate that the present contribution provides a highly discriminative and robust shape descriptor under the presence of a high level of noise, random scale variations, and low sampling rate, in addition to the known isometric-invariance property of the Laplace–Beltrami operator. The proposed method significantly outperforms state-of-the-art spectral descriptors in shape retrieval and classification. The proposed descriptor is limited to closed manifolds due to its inherited inability to accurately handle manifolds with boundaries

Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach

Multi-modal image registration is the primary step in integrating information stored in two or more images, which are captured using multiple imaging modalities. In addition to intensity variations and structural differences between images, they may have partial or full overlap, which adds an extra hurdle to the success of registration process. In this contribution, we propose a multi-modal to mono-modal transformation method that facilitates direct application of well-founded mono-modal registration methods in order to obtain accurate alignment of multi-modal images in both cases, with complete (full) and incomplete (partial) overlap. The proposed transformation facilitates recovering strong scales, rotations, and translations. We explain the method thoroughly and discuss the choice of parameters. For evaluation purposes, the effectiveness of the proposed method is examined and compared with widely used information theory-based techniques using simulated and clinical human brain images with full data. Using RIRE dataset, mean absolute error of 1.37, 1.00, and 1.41 mm are obtained for registering CT images with PD-, T1-, and T2-MRIs, respectively. In the end, we empirically investigate the efficacy of the proposed transformation in registering multi-modal partially overlapped images

Development and validation of a non-invasive, chairside oral cavity cancer risk assessment prototype using machine learning approach

Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall-precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care