Object segmentation from low depth of field images and video sequences

This thesis addresses the problem of autonomous object segmentation. To do so the proposed segementation method uses some prior information, namely that the image to be segmented will have a low depth of field and that the object of interest will be more in focus than the background. To differentiate the object from the background scene, a multiscale wavelet based assessment is proposed. The focus assessment is used to generate a focus intensity map, and a sparse fields level set implementation of active contours is used to segment the object of interest. The initial contour is generated using a grid based technique. The method is extended to segment low depth of field video sequences with each successive initialisation for the active contours generated from the binary dilation of the previous frame's segmentation. Experimental results show good segmentations can be achieved with a variety of different images, video sequences, and objects, with no user interaction or input. The method is applied to two different areas. In the first the segmentations are used to automatically generate trimaps for use with matting algorithms. In the second, the method is used as part of a shape from silhouettes 3D object reconstruction system, replacing the need for a constrained background when generating silhouettes. In addition, not using a thresholding to perform the silhouette segmentation allows for objects with dark components or areas to be segmented accurately. Some examples of 3D models generated using silhouettes are shown

Level set segmentation of retinal structures

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Changes in retinal structure are related to different eye diseases. Various retinal imaging techniques, such as fundus imaging and optical coherence tomography (OCT) imaging modalities, have been developed for non-intrusive ophthalmology diagnoses according to the vasculature changes. However, it is time consuming or even impossible for ophthalmologists to manually label all the retinal structures from fundus images and OCT images. Therefore, computer aided diagnosis system for retinal imaging plays an important role in the assessment of ophthalmologic diseases and cardiovascular disorders. The aim of this PhD thesis is to develop segmentation methods to extract clinically useful information from these retinal images, which are acquired from different imaging modalities. In other words, we built the segmentation methods to extract important structures from both 2D fundus images and 3D OCT images. In the first part of my PhD project, two novel level set based methods were proposed for detecting the blood vessels and optic discs from fundus images. The first one integrates Chan-Vese's energy minimizing active contour method with the edge constraint term and Gaussian Mixture Model based term for blood vessels segmentation, while the second method combines the edge constraint term, the distance regularisation term and the shape-prior term for locating the optic disc. Both methods include the pre-processing stage, used for removing noise and enhancing the contrast between the object and the background. Three automated layer segmentation methods were built for segmenting intra-retinal layers from 3D OCT macular and optic nerve head images in the second part of my PhD project. The first two methods combine different methods according to the data characteristics. First, eight boundaries of the intra-retinal layers were detected from the 3D OCT macular images and the thickness maps of the seven layers were produced. Second, four boundaries of the intra-retinal layers were located from 3D optic nerve head images and the thickness maps of the Retinal Nerve Fiber Layer (RNFL) were plotted. Finally, the choroidal layer segmentation method based on the Level Set framework was designed, which embedded with the distance regularisation term, edge constraint term and Markov Random Field modelled region term. The thickness map of the choroidal layer was calculated and shown.Department of Computer Science, Brunel University London

Improved modeling of segmented earthquake rupture informed by enhanced signal analysis of seismic and geodetic observations

Earthquake source modeling has emerged from the need to be able to describe and quantifythe mechanism and physical properties of earthquakes. Investigations of earthquake ruptureand fault geometry requires the testing of a large number of such potential sets of earthquakesources models. Earthquakes often rupture across more than one fault segment. If such rupturesegmentation occurs on a significant scale, a simple model may not represent the rupture processwell. This thesis focuses on the data-driven inclusion of earthquake rupture segmentation intoearthquake source modeling. The developed tools and the modeling are based on the jointuse of seismological waveform far-field and geodetic Interferometric Synthetic Aperture Radarnear-field surface displacement maps to characterise earthquake sources robustly with rigorousconsideration of data and modeling errors.A strategy based on information theory is developed to determine the appropriate modelcomplexity to represent the available observations in a data-driven way. This is done inconsideration of the uncertainties in the determined source mechanisms by investigating theinferences of the full Bayesian model ensemble. Application on the datasets of four earthquakesindicated that the inferred source parameters are systematically biased by the choice of modelcomplexity. This might have effects on follow-up analyses, e. g. regional stress field inversionsand seismic hazard assessments.Further, two methods were developed to provide data-driven model-independent constraints toinform a kinematic earthquake source optimization about earthquake source parameter priorestimates. The first method is a time-domain multi-array backprojection of teleseismic datawith empirical traveltime corrections to infer the spatio-temporal evolution of the rupture. Thisenables detection of potential rupture segmentation based on the occurrence of coherent high-frequency sources during the rupture process. The second developed method uses image analysismethods on satellite radar measured surface displacement maps to infer modeling constraints onrupture characteristics (e.g. strike and length) and the number of potential segments. These twomethods provide model-independent constraints on fault location, dimension, orientation andrupture timing. The inferred source parameter constraints are used to constrain an inversion forthe source mechanism of the 2016 Muji Mw 6.6 earthquake, a segmented and bilateral strike-slipearthquake.As a case study to further investigate a depth-segmented fault system and occurrence of co-seismic rupture segmentation in such a system the 2008-2009 Qaidam sequence with co-seismicand post-seismic displacements is investigated. The Qaidam 2008-2009 earthquake sequence innortheast Tibet involved two reverse-thrust earthquakes and a postseismic signal of the 2008earthquake. The 2008 Qaidam earthquake is modeled as a deep shallow dipping earthquakewith no indication of rupture segmentation. The 2009 Qaidam earthquake is modeled on threedistinct south-dipping high-angle thrusts, with a bilateral and segmented rupture process. Agood agreement between co-seismic surface displacement measurements and coherent seismicenergy emission in the backprojection results is determined.Finally, a combined framework is proposed which applies all the developed methods and tools inan informed parallel modeling of several earthquake source model complexities. This frameworkallows for improved routine determination of earthquake source modeling under considerationof rupture segmentation. This thesis provides overall an improvement for earthquake sourceanalyses and the development of modeling standards for robust determination of second-orderearthquake source parameters

Biometric iris image segmentation and feature extraction for iris recognition

PhD ThesisThe continued threat to security in our interconnected world today begs for urgent solution. Iris biometric like many other biometric systems provides an alternative solution to this lingering problem. Although, iris recognition have been extensively studied, it is nevertheless, not a fully solved problem which is the factor inhibiting its implementation in real world situations today. There exists three main problems facing the existing iris recognition systems: 1) lack of robustness of the algorithm to handle non-ideal iris images, 2) slow speed of the algorithm and 3) the applicability to the existing systems in real world situation. In this thesis, six novel approaches were derived and implemented to address these current limitation of existing iris recognition systems. A novel fast and accurate segmentation approach based on the combination of graph-cut optimization and active contour model is proposed to define the irregular boundaries of the iris in a hierarchical 2-level approach. In the first hierarchy, the approximate boundary of the pupil/iris is estimated using a method based on Hough’s transform for the pupil and adapted starburst algorithm for the iris. Subsequently, in the second hierarchy, the final irregular boundary of the pupil/iris is refined and segmented using graph-cut based active contour (GCBAC) model proposed in this work. The segmentation is performed in two levels, whereby the pupil is segmented first before the iris. In order to detect and eliminate noise and reflection artefacts which might introduce errors to the algorithm, a preprocessing technique based on adaptive weighted edge detection and high-pass filtering is used to detect reflections on the high intensity areas of the image while exemplar based image inpainting is used to eliminate the reflections. After the segmentation of the iris boundaries, a post-processing operation based on combination of block classification method and statistical prediction approach is used to detect any super-imposed occluding eyelashes/eyeshadows. The normalization of the iris image is achieved though the rubber sheet model. In the second stage, an approach based on construction of complex wavelet filters and rotation of the filters to the direction of the principal texture direction is used for the extraction of important iris information while a modified particle swam optimization (PSO) is used to select the most prominent iris features for iris encoding. Classification of the iriscode is performed using adaptive support vector machines (ASVM). Experimental results demonstrate that the proposed approach achieves accuracy of 98.99% and is computationally about 2 times faster than the best existing approach.Ebonyi State University and Education Task Fund, Nigeri

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

Robust Nuclei Segmentation in Cytohistopathological Images Using Statistical Level Set Approach with Topology Preserving Constraint

Computerized assessments of cyto-histological specimens have drawn increased attention in the field of digital pathology as the result of developments in digital whole slide scanners and computer hardwares. Due to the essential role of nucleus in cellular functionality, automatic segmentation of cell nuclei is a fundamental prerequisite for all cyto-histological automated systems. In 2D projection images, nuclei commonly appear to overlap each other, and the separation of severely overlapping regions is one of the most challenging tasks in computer vision. In this thesis, we will present a novel segmentation technique which effectively addresses the problem of segmenting touching or overlapping cell nuclei in cyto-histological images. The proposed framework is mainly based upon a statistical level-set approach along with a topology preserving criteria that successfully carries out the task of segmentation and separation of nuclei at the same time. The proposed method is evaluated qualitatively on Hematoxylin and Eosin stained images, and quantitatively and qualitatively on fluorescent stained images. The results indicate that the method outperforms the conventional nuclei segmentation approaches, e.g. thresholding and watershed segmentation

Anisotropic Mesh Adaptation for Image Segmentation based on Partial Differential Equations

Title from PDF of title page viewed January 12, 2021Dissertation advisor: Xianping LiVitaIncludes bibliographical references (pages 69-85)Thesis (Ph.D.)--Department of Mathematics and Statistics and School of Computing and Engineering. University of Missouri--Kansas City, 2020As the resolution of digital images increases significantly, the processing of images becomes more challenging in terms of accuracy and efficiency. In this dissertation, we consider image segmentation by solving a partial differential equation (PDE) model based on the Mumford-Shah functional. We first, develop a new anisotropic mesh adaptation (AMA) framework to improve segmentation efficiency and accuracy. In the AMA framework, we incorporate an anisotropic mesh adaptation for image representation and a nite element method for solving the PDE model. Comparing to traditional algorithms solved by the finnite difference method, our AMA framework provides faster and better results without the need for re-sizing the images to lower quality. We also extend the algorithm to segment images with multiple regions. We also improve the well-known Chan-Vese model by developing a locally enhanced Chan-Vese (LECV) model. Our LECV model incorporates a newly define signed pressure force (SPF) function, which is built upon the local image information. The SPF function helps to attract the contour curve to the object boundaries for images with inhomogeneous intensities. The proposed LECV model, together with the AMA segmentation framework can successfully segment the image with or without inhomogeneous intensities. While most other segmentation methods only work on low-resolution images, our LECV model is successfully applied to high-resolution images, with improved efficiency and accuracy.Introduction -- PDE-Based Image Segmentation -- Background and Literature review -- AMA Segmentation Method -- LECV Model for Image Segmentation -- Conclusion and discussio

Image Segmentation and Its Applications Based on the Mumford-Shah Model

Image segmentation is an important topic in computer vision and image processing. As a region-based (global) approach, the Mumford and Shah (MS) model is a powerful and robust segmentation technique as compared to edge-based (local) methods. In this thesis we apply the MS model to two interesting problems: image inpainting and text line detection. We further extend it by proposing a new image segmentation model to overcome some of the difficulties of the original model. As a demonstration of the new model, we apply it to the segmentation of retinal images. The results are better than the state-of-the-art approaches. In image inpainting, the MS model is used to detect and estimate the object boundaries inside the inpainting areas. These boundaries are preserved in the inpainting results. We present a hierarchical segmentation method to detect boundaries of both the main structure and the details. The inpainting result can preserve detailed edges. In text line detection, we use a combination of Gaussian blurring, the MS model, and morphing method. Different from other general text image detection approaches, our method segments text documents without any knowledge of the written texts, so it can detect handwriting text lines of different languages. It can also handle different gaps and overlaps among the text lines. Although the MS model has been used successfully in many applications, its implementation has always been based on some forms of approximation. These approximations are either inefficient computationally or applicable only to some special cases. Our new model consists of only one variable, the segmentation curve, therefore the computation is very efficient. Furthermore, no approximation is required, hence the method can segment objects with complicated intensity distribution. The new model can detect both step and roof edges, and can use different filters to detect objects of different levels of intensity. To show the advantages of the new model, we use a combination of the new model and Gabor filter to detect blood vessels in retinal images. This new model can detect objects with complicated image intensity distribution, and can handle non-uniform illumination cases effectively