MODELING AND ANALYSIS OF WRINKLES ON AGING HUMAN FACES

The analysis and modeling of aging human faces has been extensively studied in the past decade. Most of this work is based on matching learning techniques focused on appearance of faces at different ages incorporating facial features such as face shape/geometry and patch-based texture features. However, we do not find much work done on the analysis of facial wrinkles in general and specific to a person. The goal of this dissertation is to analyse and model facial wrinkles for different applications. Facial wrinkles are challenging low-level image features to analyse. In general, skin texture has drastically varying appearance due to its characteristic physical properties. A skin patch looks very different when viewed or illuminated from different angles. This makes subtle skin features like facial wrinkles difficult to be detected in images acquired in uncontrolled imaging settings. In this dissertation, we examine the image properties of wrinkles i.e. intensity gradients and geometric properties and use them for several applications including low-level image processing for automatic detection/localization of wrinkles, soft biometrics and removal of wrinkles using digital inpainting. First, we present results of detection/localization of wrinkles in images using Marked Point Process (MPP). Wrinkles are modeled as sequences of line segments in a Bayesian framework which incorporates a prior probability model based on the likely geometric properties of wrinkles and a data likelihood term based on image intensity gradients. Wrinkles are localized by sampling the posterior probability using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We also present an evaluation algorithm to quantitatively evaluate the detection and false alarm rate of our algorithm and conduct experiments with images taken in uncontrolled settings. The MPP model, despite its promising localization results, requires a large number of iterations in the RJMCMC algorithm to reach global minimum resulting in considerable computation time. This motivated us to adopt a deterministic approach based on image morphology for fast localization of facial wrinkles. We propose image features based on Gabor filter banks to highlight subtle curvilinear discontinuities in skin texture caused by wrinkles. Then, image morphology is used to incorporate geometric constraints to localize curvilinear shapes of wrinkles at image sites of large Gabor filter responses. We conduct experiments on two sets of low and high resolution images to demonstrate faster and visually better localization results as compared to those obtained by MPP modeling. As a next application, we investigate the user-drawn and automatically detected wrinkles as a pattern for their discriminative power as a soft biometrics to recognize subjects from their wrinkle patterns only. A set of facial wrinkles from an image is treated as a curve pattern and used for subject recognition. Given the wrinkle patterns from a query and gallery images, several distance measures are calculated between the two patterns to quantify the similarity between them. This is done by finding the possible correspondences between curves from the two patterns using a simple bipartite graph matching algorithm. Then several metrics are used to calculate the similarity between the two wrinkle patterns. These metrics are based on Hausdorff distance and curve-to-curve correspondences. We conduct experiments on data sets of both hand drawn and automatically detected wrinkles. Finally, we apply digital inpainting to automatically remove wrinkles from facial images. Digital image inpainting refers to filling in the holes of arbitrary shapes in images so that they seem to be part of the original image. The inpainting methods target either the structure or the texture of an image or both. There are two limitations of existing inpainting methods for the removal of wrinkles. First, the differences in the attributes of structure and texture requires different inpainting methods. Facial wrinkles do not fall strictly under the category of structure or texture and can be considered as some where in between. Second, almost all of the image inpainting techniques are supervised i.e. the area/gap to be filled is provided by user interaction and the algorithms attempt to find the suitable image portion automatically. We present an unsupervised image inpainting method where facial regions with wrinkles are detected automatically using their characteristic intensity gradients and removed by painting the regions by the surrounding skin texture

Fusing spatial and temporal components for real-time depth data enhancement of dynamic scenes

The depth images from consumer depth cameras (e.g., structured-light/ToF devices) exhibit a substantial amount of artifacts (e.g., holes, flickering, ghosting) that needs to be removed for real-world applications. Existing methods cannot entirely remove them and perform slow. This thesis proposes a new real-time spatio-temporal depth image enhancement filter that completely removes flickering and ghosting, and significantly reduces holes. This thesis also presents a novel depth-data capture setup and two data reduction methods to optimize the performance of the proposed enhancement method

GAN-Based Super-Resolution And Segmentation Of Retinal Layers In Optical Coherence Tomography Scans

Optical Coherence Tomography (OCT) has been identified as a noninvasive and cost-effective imaging modality for identifying potential biomarkers for Alzheimer\u27s diagnosis and progress detection. Current hypotheses indicate that retinal layer thickness, which can be assessed via OCT scans, is an efficient biomarker for identifying Alzheimer\u27s disease. Due to factors such as speckle noise, a small target region, and unfavorable imaging conditions manual segmentation of retina layers is a challenging task. Therefore, as a reasonable first step, this study focuses on automatically segmenting retinal layers to separate them for subsequent investigations. Another important challenge commonly faced is the lack of clarity of the layer boundaries in retina OCT scans, which compels the research of super-resolving the images for improved clarity. Deep learning pipelines have stimulated substantial progress for the segmentation tasks. Generative adversarial networks (GANs) are a prominent field of deep learning which achieved astonishing performance in semantic segmentation. Conditional adversarial networks as a general-purpose solution to image-to-image translation problems not only learn the mapping from the input image to the output image but also learn a loss function to train this mapping. We propose a GAN-based segmentation model and evaluate incorporating popular networks, namely, U-Net and ResNet, in the GAN architecture with additional blocks of transposed convolution and sub-pixel convolution for the task of upscaling OCT images from low to high resolution by a factor of four. We also incorporate the Dice loss as an additional reconstruction loss term to improve the performance of this joint optimization task. Our best model configuration empirically achieved the Dice coefficient of 0.867 and mIOU of 0.765

Segmentation and lesion detection in dermoscopic images

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonMalignant melanoma is one of the most fatal forms of skin cancer. It has also become increasingly common, especially among white-skinned people exposed to the sun. Early detection of melanoma is essential to raise survival rates, since its detection at an early stage can be helpful and curable. Working out the dermoscopic clinical features (pigment network and lesion borders) of melanoma is a vital step for dermatologists, who require an accurate method of reaching the correct clinical diagnosis, and ensure the right area receives the correct treatment. These structures are considered one of the main keys that refer to melanoma or non-melanoma disease. However, determining these clinical features can be a time-consuming, subjective (even for trained clinicians) and challenging task for several reasons: lesions vary considerably in size and colour, low contrast between an affected area and the surrounding healthy skin, especially in early stages, and the presence of several elements such as hair, reflections, oils and air bubbles on almost all images. This thesis aims to provide an accurate, robust and reliable automated dermoscopy image analysis technique, to facilitate the early detection of malignant melanoma disease. In particular, four innovative methods are proposed for region segmentation and classification, including two for pigmented region segmentation, one for pigment network detection, and one for lesion classification. In terms of boundary delineation, four pre-processing operations, including Gabor filter, image sharpening, Sobel filter and image inpainting methods are integrated in the segmentation approach to delete unwanted objects (noise), and enhance the appearance of the lesion boundaries in the image. The lesion border segmentation is performed using two alternative approaches. The Fuzzy C-means and the Markov Random Field approaches detect the lesion boundary by repeating the labeling of pixels in all clusters, as a first method. Whereas, the Particle Swarm Optimization with the Markov Random Field method achieves greater accuracy for the same aim by combining them in the second method to perform a local search and reassign all image pixels to its cluster properly. With respect to the pigment network detection, the aforementioned pre-processing method is applied, in order to remove most of the hair while keeping the image information and increase the visibility of the pigment network structures. Therefore, a Gabor filter with connected component analysis are used to detect the pigment network lines, before several features are extracted and fed to the Artificial Neural Network as a classifier algorithm. In the lesion classification approach, the K-means is applied to the segmented lesion to separate it into homogeneous clusters, where important features are extracted; then, an Artificial Neural Network with Radial Basis Functions is trained by representative features to classify the given lesion as melanoma or not. The strong experimental results of the lesion border segmentation methods including Fuzzy C-means with Markov Random Field and the combination between the Particle Swarm Optimization and Markov Random Field, achieved an average accuracy of 94.00% , 94.74% respectively. Whereas, the lesion classification stage by using extracted features form pigment network structures and segmented lesions achieved an average accuracy of 90.1% , 95.97% respectively. The results for the entire experiment were obtained using a public database PH2 comprising 200 images. The results were then compared with existing methods in the literature, which have demonstrated that our proposed approach is accurate, robust, and efficient in the segmentation of the lesion boundary, in addition to its classification

Accurate 3D-reconstruction and -navigation for high-precision minimal-invasive interventions

The current lateral skull base surgery is largely invasive since it requires wide exposure and direct visualization of anatomical landmarks to avoid damaging critical structures. A multi-port approach aiming to reduce such invasiveness has been recently investigated. Thereby three canals are drilled from the skull surface to the surgical region of interest: the first canal for the instrument, the second for the endoscope, and the third for material removal or an additional instrument. The transition to minimal invasive approaches in the lateral skull base surgery requires sub-millimeter accuracy and high outcome predictability, which results in high requirements for the image acquisition as well as for the navigation. Computed tomography (CT) is a non-invasive imaging technique allowing the visualization of the internal patient organs. Planning optimal drill channels based on patient-specific models requires high-accurate three-dimensional (3D) CT images. This thesis focuses on the reconstruction of high quality CT volumes. Therefore, two conventional imaging systems are investigated: spiral CT scanners and C-arm cone-beam CT (CBCT) systems. Spiral CT scanners acquire volumes with typically anisotropic resolution, i.e. the voxel spacing in the slice-selection-direction is larger than the in-the-plane spacing. A new super-resolution reconstruction approach is proposed to recover images with high isotropic resolution from two orthogonal low-resolution CT volumes. C-arm CBCT systems offers CT-like 3D imaging capabilities while being appropriate for interventional suites. A main drawback of these systems is the commonly encountered CT artifacts due to several limitations in the imaging system, such as the mechanical inaccuracies. This thesis contributes new methods to enhance the CBCT reconstruction quality by addressing two main reconstruction artifacts: the misalignment artifacts caused by mechanical inaccuracies, and the metal-artifacts caused by the presence of metal objects in the scanned region. CBCT scanners are appropriate for intra-operative image-guided navigation. For instance, they can be used to control the drill process based on intra-operatively acquired 2D fluoroscopic images. For a successful navigation, accurate estimate of C-arm pose relative to the patient anatomy and the associated surgical plan is required. A new algorithm has been developed to fulfill this task with high-precision. The performance of the introduced methods is demonstrated on simulated and real data

Advanced methods for mapping the radiofrequency magnetic fields in MRI

As MRI systems have increased in static magnetic field strength, the radiofrequency (RF) fields that are used for magnetisation excitation and signal reception have become significantly less uniform. This can lead to image artifacts and errors when performing quantitative MRI. A further complication arises if the RF fields vary substantially in time. In the first part of this investigation temporal variations caused by respiration were explored on a 3T scanner. It was found that fractional changes in transmit field amplitude between inhalation and expiration ranged from 1% to 14% in the region of the liver in a small group of normal subjects. This observation motivated the development of a pulse sequence and reconstruction method to allow dynamic observation of the transmit field throughout the respiratory cycle. However, the proposed method was unsuccessful due to the inherently time-consuming nature of transmit field mapping sequences. This prompted the development of a novel data reconstruction method to allow the acceleration of transmit field mapping sequences. The proposed technique posed the RF field reconstruction as a nonlinear least-squares optimisation problem, exploiting the fact that the fields vary smoothly. It was shown that this approach was superior to standard reconstruction approaches. The final component of this thesis presents a unified approach to RF field calibration. The proposed method uses all measured data to estimate both transmit and receive sensitivities, whilst simultaneously insisting that they are smooth functions of space. The resulting maps are robust to both noise and imperfections in regions of low signal

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium