Multi-resolution Active Models for Image Segmentation

Image segmentation refers to the process of subdividing an image into a set of non-overlapping regions. Image segmentation is a critical and essential step to almost all higher level image processing and pattern recognition approaches, where a good segmentation relieves higher level applications from considering irrelevant and noise data in the image. Image segmentation is also considered as the most challenging image processing step due to several reasons including spatial discontinuity of the region of interest and the absence of a universally accepted criteria for image segmentation. Among the huge number of segmentation approaches, active contour models or simply snakes receive a great attention in the literature. Where the contour/boundary of the region of interest is defined as the set of pixels at which the active contour reaches its equilibrium state. In general, two forces control the movement of the snake inside the image, internal force that prevents the snake from stretching and bending and external force that pulls the snake towards the desired object boundaries. One main limitation of active contour models is their sensitivity to image noise. Specifically, noise sensitivity leads the active contour to fail to properly converge, getting caught on spurious image features, preventing the iterative solver from taking large steps towards the final contour. Additionally, active contour initialization forms another type of limitation. Where, especially in noisy images, the active contour needs to be initialized relatively close to the object of interest, otherwise the active contour will be pulled by other non-real/spurious image features. This dissertation, aiming to improve the active model-based segmentation, introduces two models for building up the external force of the active contour. The first model builds up a scale-based-weighted gradient map from all resolutions of the undecimated wavelet transform, with preference given to coarse gradients over fine gradients. The undecimated wavelet transform, due to its near shift-invariance and the absence of down-sampling properties, produces well-localized gradient maps at all resolutions of the transform. Hence, the proposed final weighted gradient map is able to better drive the snake towards its final equilibrium state. Unlike other multiscale active contour algorithms that define a snake at each level of the hierarchy, our model defines a single snake with the external force field is simultaneously built based on gradient maps from all scales. The second model proposes the incorporation of the directional information, revealed by the dual tree complex wavelet transform (DT CWT), into the external force field of the active contour. At each resolution of the transform, a steerable set of convolution kernels is created and used for external force generation. In the proposed model, the size and the orientation of the kernels depend on the scale of the DT CWT and the local orientation statistics of each pixel. Experimental results using nature, synthetic and Optical Coherent Tomography (OCT) images reflect the superiority of the proposed models over the classical and the state-of-the-art models

A new approach for quantifying epithelial and stromal thickness changes after orthokeratology contact lens wear

The aim of the study was to develop an automatic segmentation approach to optical coherence tomography (OCT) images and to investigate the changes in epithelial and stromal thickness profile and radius of curvature after the use of orthokeratology (Ortho-K) contact lenses. A total of 45 right eyes from 52 participants were monitored before, and after one month of, uninterrupted overnight Ortho-K lens wear. The tomography of their right eyes was obtained using optical OCT and rotating Scheimpflug imaging (OCULUS Pentacam). A custom-built MATLAB code for automatic segmentation of corneal OCT images was created and used to assess changes in epithelial thickness, stromal thickness, corneal and stromal profiles and radii of curvature before, and after one month of, uninterrupted overnight wear of Ortho-K lenses. In the central area (0–2 mm diameter), the epithelium thinned by 12.8 ± 6.0 µm (23.8% on average, p < 0.01) after one month of Ortho-K lens wear. In the paracentral area (2–5 mm diameter), the epithelium thinned nasally and temporally (by 2.4 ± 5.9 µm, 4.5% on average, p = 0.031). The stroma thickness increased in the central area (by 4.8 ± 16.1 µm, p = 0.005). The radius of curvature of the central corneal anterior surface increased by 0.24 ± 0.26 mm (3.1%, p < 0.01) along the horizontal meridian and by 0.34 ± 0.18 mm (4.2%, p < 0.01) along the vertical meridian. There were no significant changes in the anterior and posterior stromal radius of curvature. This study introduced a new method to automatically detect the anterior corneal surface, the epithelial posterior surface and the posterior corneal surface in OCT scans. Overnight wear of Ortho-K lenses caused thinning of the central corneal epithelium. The anterior corneal surface became flattered while the anterior and posterior surfaces of the stroma did not undergo significant changes. The results are consistent with the changes reported in previous studies. The reduction in myopic refractive error caused by Ortho-K lens wear was mainly due to changes in corneal epithelium thickness profile

A new method for detecting the outer corneal contour in images from an ultra‑fast Scheimpflug camera

BACKGROUND: The Corvis® ST tonometer is an innovative device which, by combining a classic non-contact tonometer with an ultra-fast Scheimpflug camera, provides a number of parameters allowing for the assessment of corneal biomechanics. The acquired biomechanical parameters improve medical diagnosis of selected eye diseases. One of the key elements in biomechanical measurements is the correct corneal contour detection, which is the basis for further calculations. The presented study deals with the problem of outer corneal edge detection based on a series of images from the afore-mentioned device. Corneal contour detection is the first and extremely important stage in the acquisition and analysis of corneal dynamic parameters. RESULT: A total of 15,400 images from the Corvis® ST tonometer acquired from 110 patients undergoing routine ophthalmologic examinations were analysed. A method of outer corneal edge detection on the basis of a series of images from the Corvis® ST was proposed. The method was compared with known and commonly used edge detectors: Sobel, Roberts, and Canny operators, as well as others, known from the literature. The analysis was carried out in MATLAB® version 9.0.0.341360 (R2016a) with the Image Processing Toolbox (version 9.4) and the Neural Network Toolbox (version 9.0). The method presented in this paper provided the smallest values of the mean error (0.16%), stability (standard deviation 0.19%) and resistance to noise, characteristic for Corvis® ST tonometry tests, compared to the methods known from the literature. The errors were 5.78 ± 9.19%, 3.43 ± 6.21%, and 1.26 ± 3.11% for the Roberts, Sobel, and Canny methods, respectively. CONCLUSIONS: The proposed new method for detecting the outer corneal contour increases the accuracy of intraocular pressure measurements. It can be used to analyse dynamic parameters of the cornea

NOVEL METHODS OF MERIDIONAL AND CIRCUMFERENTIAL ANTERIOR CHAMBER ANGLE IMAGING

Ph.DDOCTOR OF PHILOSOPH

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking

The first step in monitoring an observer’s eye gaze is identifying and locating the image of their pupils in video recordings of their eyes. Current systems work under a range of conditions, but fail in bright sunlight and rapidly varying illumination. A computer vision system was developed to assist with the recognition of the pupil in every frame of a video, in spite of the presence of strong first-surface reflections off of the cornea. A modified Hough Circle detector was developed that incorporates knowledge that the pupil is darker than the surrounding iris of the eye, and is able to detect imperfect circles, partial circles, and ellipses. As part of processing the image is modified to compensate for the distortion of the pupil caused by the out-of-plane rotation of the eye. A sophisticated noise cleaning technique was developed to mitigate first surface reflections, enhance edge contrast, and reduce image flare. Semi-supervised human input and validation is used to train the algorithm. The final results are comparable to those achieved using a human analyst, but require only a tenth of the human interaction

Image analytic tools for tissue characterization using optical coherence tomography

Optical coherence tomography (OCT) has been emerging as a promising imaging technique, with a strong capability of non-invasive, in vivo, high resolution, depth-resolved imaging. There is a great potential to use OCT to guide the treatment of arrhythmias, to prevent preterm birth, and to detect breast cancer. To facilitate the clinical applications, this thesis presents three image analytic tools to characterize biological tissue: 1) automated fiber direction analysis; 2) automated volumetric stitching; 3) automated tissue classification. The fiber direction analysis consists of a particle-filter-based 3D tractography scheme and a pixel-wise fiber analysis scheme. The stitching algorithm enlarges the field of view of current OCT system from millimeter to centimeter level by volumetric stitching using scale-invariant feature transform. Based on relevance vector machine, a region-based classification scheme and a grid-based classification scheme are developed to automatically identify tissue composition in human cardiac tissue and human breast tissue. These tools are collaboratively used to study OCT images from cardiac, cervical, and breast tissue. In cardiac tissue, we apply the fiber orientation analysis to reconstruct 3D cardiac myofibers tractography and perform pixel-wise fiber analysis on the collagen region within human heart. In addition, we apply the region-based algorithm to segment and classify tissue compositions, such as collagen, adipose tissue, fibrotic myocardium, and normal myocardium, over a single or a stitched OCT volume. Using our algorithm, we observe fiber directionality change over depths and find that the fiber orientation changes more dramatically in atria than in ventricle. We also observe different dispersion patterns within collagen layer. In cervical tissue, our stitching algorithm enables a paramount 3D view of entire axial slices. Together with pixel-wise fiber orientation scheme, we analyze the difference of dispersion property within inner/outer regions of four quadrants. We observe two dispersion patterns in pregnant and non-pregnant cervical tissue at the location close to upper cervix. In addition, we discover that an increasing trend of dispersion and an increasing trend of penetration depth from internal orifice (os) to external os. In breast tissue, we visualize various features in both benign and malignant tissues such as invasive ductal carcinoma (IDC), ductal carcinoma in situ, cyst, and terminal duct lobule unit in stitched OCT images. Focusing on the automated detection of IDC, we propose a hierarchy framework of classification model and apply our classifier in two OCT systems and achieve both reasonable sensitivity and specificity in identifying cancerous region

An automated image processing system for the detection of photoreceptor cells in adaptive optics retinal images

The rapid progress in Adaptive Optics (AO) imaging, in the last decades, has had a transformative impact on the entire approach underpinning the investigations of retinal tissues. Capable of imaging the retina in vivo at the cellular level, AO systems have revealed new insights into retinal structures, function, and the origins of various retinal pathologies. This has expanded the field of clinical research and opened a wide range of applications for AO imaging. The advances in image processing techniques contribute to a better observation of retinal microstructures and therefore more accurate detection of pathological conditions. The development of automated tools for processing images obtained with AO allows for objective examination of a larger number of images with time and cost savings and thus facilitates the use of AO imaging as a practical and efficient tool, by making it widely accessible to the clinical ophthalmic community. In this work, an image processing framework is developed that allows for enhancement of AO high-resolution retinal images and accurate detection of photoreceptor cells. The proposed framework consists of several stages: image quality assessment, illumination compensation, noise suppression, image registration, image restoration, enhancement and detection of photoreceptor cells. The visibility of retinal features is improved by tackling specific components of the AO imaging system, affecting the quality of acquired retinal data. Therefore, we attempt to fully recover AO retinal images, free from any induced degradation effects. A comparative study of different methods and evaluation of their efficiency on retinal datasets is performed by assessing image quality. In order to verify the achieved results, the cone packing density distribution was calculated and correlated with statistical histological data. From the performed experiments, it can be concluded that the proposed image processing framework can effectively improve photoreceptor cell image quality and thus can serve as a platform for further investigation of retinal tissues. Quantitative analysis of the retinal images obtained with the proposed image processing framework can be used for comparison with data related to pathological retinas, as well as for understanding the effect of age and retinal pathology on cone packing density and other microstructures

Improving the structure function relationship in the macula

The macula is the central part of the retina responsible for central vision and can suffer damage from many diseases, including diabetes, macular degeneration and glaucoma. Establishing a relationship between functional measurements, such as perimetry, and structural metrics, such as those obtained through imaging, has proven both clinically appealing and challenging, owing to specific features of this area of the retina. The programme of work presented in this thesis focuses on improving the accuracy of structure-function analyses of the macula as well as the mechanistic understanding of structure-function relationship in both healthy and diseased eyes. The first study revisits and improves previous models quantifying the length of Henle’s fibres. This directly relates to the radial displacement of Retinal Ganglion Cells (RGCs) from their photoreceptors and affects structure-function mapping. The study demonstrated the inaccuracy of previous methods used to displace perimetric stimuli, proposing a correct implementation of these calculations. These results were made available to other researchers in a user-friendly web application. The second study explored how natural positioning of observers in front of imaging and perimetry devices, as well as their fixation and eye movements, affected the precision of macular structure-function mapping. The study analysed data from an eye-tracking perimeter used to test both healthy eyes and patients with glaucoma. An optimal strategy for structure-function mapping was developed and the mapping error introduced by fixation was quantified. The third study used data from an eye-tracking perimeter and the framework of an established neural model of spatial summation to investigate the structure-function relationship in early neural loss in patients with diabetes without diabetic retinopathy, quantified with both imaging and functional tests, including Frequency Doubling Perimetry, standard visual acuity and contrast sensitivity. The fourth study involved the prospective collection of data from healthy observers with perimetric stimuli of different sizes and durations, using custom software. The data were used to develop a computational model of perimetric sensitivity able to reproduce the interaction between spatial and temporal summation in the context of cortical integration and their link to the number of retinal ganglion cells being stimulated. In the fifth study, the methodology and mechanistic framework developed in the previous studies were applied to test the computational model in glaucoma. The model was used to obtain functional estimates of retinal ganglion cell damage from standard automated perimetry data collected in glaucoma patients and healthy age-related controls. The results were correlated with imaging and histology data from previous literature