During vertebrate embryo development, the body axis is subdivided into repeated structures, called somites. Somites bud off from an un-segmented tissue called the pre-somitic mesoderm (PSM) in a sequential and periodic manner, tightly controlled by an in built molecular clock, called the "segmentation clock". According to current understanding, the clock is comprised of: (i) an autonomous cellular oscillator consisting of an intracellular negative feedback loop of Her genes within the PSM cells, (ii) Delta-ligand and Notch-receptor coupling that facilitates synchronization of oscillators among the PSM cells, (iii) Tissue level waves of gene expression that emerge in the posterior PSM and move coherently towards anterior, leading to global arrest of oscillations in the form of somites. However, the movement of cellular oscillators within the PSM before the formation of somitic furrows, a prominent feature of the tissue as observed through this work has not been experimentally considered as a constituent of the segmentation clock so far. Our work aims to incorporate movement of cellular oscillators in the framework of the segmentation clock. It is well known from theoretical studies that the characteristics of relative motion of oscillators affect their synchronization properties and the patterns of oscillations they form. Particularly, theoretical studies by Uriu et al., PNAS (2010) suggest that cell movements promotes synchronization of genetic oscillations. Here, we established experimental techniques and image analysis tools to attain quantitative insight on (i) diffusion co-efficient of cellular oscillators, (ii) dynamics of a population of oscillators, within the PSM aiming towards concomitant understanding of the relationship between movement and synchronization of cellular oscillators. In order to quantitatively relate cellular oscillators and their motion within the PSM, I established imaging techniques that enabled visualization of fluorescenctly labeled nuclei as readouts of cell positions in live embryo, and developed dedicated segmentation algorithm and implemented tracking protocol to obtain nuclei positions over time in 3D space. Furthermore, I provide benchmarking techniques in the form of artificial data that validate segmentation algorithm efficacy and, for the first time proposed the use of transgenic embryo chimeras to validate segmentation algorithm performance within the context of in vivo live imaging of embryonic tissues. Preliminary analysis of our data suggests that there is relatively high cell mixing in the posterior PSM, within the same spatial zone where synchronous oscillations emerge at maximum speed. Also, there are indications of gradient of cell mixing along the anterior-posterior axis of the embryo. By sampling single cell tracks with the help of nuclei markers, we have also been able to follow in vivo protein oscillations at single cell resolution that would allow quantitative characterization of coherence among a population of cellular oscillators over time. Our image analysis work flow allows testing of mutant embryos and perturbation of synchrony dynamics to understand the cause-effect relationship between movement and synchronization properties at cellular resolution. Essentially, through this work, we hope to bridge the time scales of events and cellular level dynamics that leads to highly coordinated tissue level patterns and thereby further our understanding of the segmentation clock mechanism.

The haptic exploration of 2-D images is a challenging problem in computer haptics. Research on the topic has primarily been focused on the exploration of maps and curves. This thesis describes the design and implementation of a system for the haptic exploration of photographs. The system builds on various research directions related to assistive technology, computer haptics, and image segmentation. An object-level segmentation hierarchy is generated from the source photograph to be rendered haptically as a contour image at multiple levels-of-detail. A tool for the authoring of object-level hierarchies was developed, as well as an innovative type of user interaction by region selection for accurate and efficient image segmentation. According to an objective benchmark measuring how the new method compares with other interactive image segmentation algorithms shows that our region selection interaction is a viable alternative to marker-based interaction. The hierarchy authoring tool combined with precise algorithms for image segmentation can build contour images of the quality necessary for the images to be understood by touch with our system. The system was evaluated with a user study of 24 sighted participants divided in different groups. The first part of the study had participants explore images using haptics and answer questions about them. The second part of the study asked the participants to identify images visually after haptic exploration. Results show that using a segmentation hierarchy supporting multiple levels-of-detail of the same image is beneficial to haptic exploration. As the system gains maturity, it is our goal to make it available to blind users.

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Many research questions in biological research lead to numerous microscope images that need to be evaluated. Here digital image cytometry, i.e., quantitative, automated or semi-automated analysis of the images is an important rapidly growing discipline. This thesis presents contributions to that field. The work has been carried out in close cooperation with biomedical research partners, successfully solving real world problems. The world is 3D and modern imaging methods such as confocal microscopy provide 3D images. Hence, a large part of the work has dealt with the development of new and improved methods for quantitative analysis of 3D images, in particular fluorescently labeled skeletal muscle cells. A geometrical model for robust segmentation of skeletal muscle fibers was developed. Images of the multinucleated muscle cells were pre-processed using a novel spatially modulated transform, producing images with reduced complexity and facilitating easy nuclei segmentation. Fibers from several mammalian species were modeled and features were computed based on cell nuclei positions. Features such as myonuclear domain size and nearest neighbor distance, were shown to correlate with body mass, and femur length. Human muscle fibers from young and old males, and females, were related to fiber type and extracted features, where myonuclear domain size variations were shown to increase with age irrespectively of fiber type and gender. A segmentation method for severely clustered point-like signals was developed and applied to images of fluorescent probes, quantifying the amount and location of mitochondrial DNA within cells. A synthetic cell model was developed, to provide a controllable golden standard for performance evaluation of both expert manual and fully automated segmentations. The proposed method matches the correctness achieved by manual quantification. An interactive segmentation procedure was successfully applied to treated testicle sections of boar, showing how a common industrial plastic softener significantly affects testosterone concentrations.

La segmentación de imágenes es una técnica que tiene como finalidad dividir una imagen en un conjunto de regiones, asignando a cada objeto en la escena una o varias regiones. Para obtener una segmentación correcta, cada una de las regiones debe cumplir con un criterio de homogeneidad impuesto a priori. Cuando se fija un criterio de homogeneidad, lo que implícitamente se esta haciendo es asumir un modelo matemático que caracteriza las regiones.En esta tesis se introduce un nuevo tipo de modelo denominado modelo jerárquico, ya que tiene dos niveles diferentes sobrepuestos uno sobre el otro. El nivel inferior (o subyacente) modela la posición que ocupa cada una de las regiones dentro de la imagen; mientras que, por su parte, el nivel superior (u observable) esta compuesto por un conjunto de submodelos independientes (un submodelo por región) que caracterizan el comportamiento del interior de las regiones. Para el primero se usa un campo aleatorio Markoviano de orden dos que modelara los contornos de las regiones, mientras que para el segundo nivel se usa un modelo Gausiano. En el trabajo se estudian los mejores potenciales que deben asignarse a los tipos de agrupaciones que permiten definir los contornos. Con todo ello la segmentación se realiza buscando la partición más probable (criterio MAP) para una realización concreta (imagen observable).El proceso de búsqueda de la partición optima para imágenes del tamaño habitual seria prácticamente inviable desde un punto de vista de tiempo de cálculo. Para que se pueda realizar debe partirse de una estimación inicial suficientemente buena y de una algoritmo rápido de mejora como es una búsqueda local. Para ello se introduce la técnica de segmentación piramidal (multirresolucion). La pirámide se genera con filtrado Gausiano y diezmado. En el nivel mas alto de la pirámide, al tener pocos píxels, si que se puede encontrar la partición óptima.

Texture analysis has been used extensively in the computer-assisted interpretation of SAR sea ice imagery. Provision of maps which distinguish relevant ice types is significant for monitoring global warming and ship navigation. Due to the abundance of SAR imagery available, there exists a need to develop an automated approach for SAR sea ice interpretation. Grey level co-occurrence probability (<i>GLCP</i>) texture features are very popular for SAR sea ice classification. Although these features are used extensively in the literature, they have a tendency to erode and misclassify texture boundaries. Proposed is an advancement to the <i>GLCP</i> method which will preserve texture boundaries during image segmentation. This method exploits the relationship a pixel has with its closest neighbors and weights the texture measurement accordingly. These texture features are referred to as <i>WGLCP</i> (weighted <i>GLCP</i>) texture features. In this research, the <i>WGLCP</i> and <i>GLCP</i> feature sets are compared in terms of boundary preservation, unsupervised segmentation ability, robustness to increasing boundary density and computation time. The <i>WGLCP</i> method outperforms the <i>GLCP</i> method in all aspects except for computation time, where it suffers. From the comparative analysis, an inconsistency with the <i>GLCP</i> correlation statistic was observed, which motivated an investigative study into using this statistic for image segmentation. As the overall goal of the thesis is to improve SAR sea ice segmentation accuracy, the concepts developed from the study are applied to the image segmentation problem. The results indicate that for images with high contrast boundaries, the <i>GLCP</i> correlation statistical feature decreases segmentation accuracy. When comparing <i>WGLCP</i> and <i>GLCP</i> features for segmentation, the <i>WGLCP</i> features provide higher segmentation accuracy.

Speech Endpoint Detection, also known as Speech Segmentation, is an unsolved problem in speech processing that affects numerous applications including robust speech recognition. This task is not as trivial as it appears, and most of the existing algorithms degrade at low signal-to-noise ratios (SNRs). Most of the previous research approaches have focused on the development of robust algorithms with special attention being paid to the derivation and study of noise robust features and decision rules. This research tackles the endpoint detection problem in a different way, and proposes a novel speech endpoint detection algorithm which has been derived from Chan-Vese algorithm for image segmentation. The proposed algorithm has the ability to fuse multi features extracted from the speech signal to enhance the detection accuracy. The algorithm performance has been evaluated and compared to two widely used speech detection algorithms under various noise environments with SNR levels ranging from 0 dB to 30 dB. Furthermore, the proposed algorithm has also been applied to different types of American English phonemes. The experiments show that, even under conditions of severe noise contamination, the proposed algorithm is more efficient as compared to the reference algorithms.

Texture analysis has been used extensively in the computer-assisted interpretation of SAR sea ice imagery. Provision of maps which distinguish relevant ice types is significant for monitoring global warming and ship navigation. Due to the abundance of SAR imagery available, there exists a need to develop an automated approach for SAR sea ice interpretation. Grey level co-occurrence probability (<i>GLCP</i>) texture features are very popular for SAR sea ice classification. Although these features are used extensively in the literature, they have a tendency to erode and misclassify texture boundaries. Proposed is an advancement to the <i>GLCP</i> method which will preserve texture boundaries during image segmentation. This method exploits the relationship a pixel has with its closest neighbors and weights the texture measurement accordingly. These texture features are referred to as <i>WGLCP</i> (weighted <i>GLCP</i>) texture features. In this research, the <i>WGLCP</i> and <i>GLCP</i> feature sets are compared in terms of boundary preservation, unsupervised segmentation ability, robustness to increasing boundary density and computation time. The <i>WGLCP</i> method outperforms the <i>GLCP</i> method in all aspects except for computation time, where it suffers. From the comparative analysis, an inconsistency with the <i>GLCP</i> correlation statistic was observed, which motivated an investigative study into using this statistic for image segmentation. As the overall goal of the thesis is to improve SAR sea ice segmentation accuracy, the concepts developed from the study are applied to the image segmentation problem. The results indicate that for images with high contrast boundaries, the <i>GLCP</i> correlation statistical feature decreases segmentation accuracy. When comparing <i>WGLCP</i> and <i>GLCP</i> features for segmentation, the <i>WGLCP</i> features provide higher segmentation accuracy.

Real-time inspection based on machine vision technologies is being widely used in quality control and cost reduction in a variety of application domains. The high demands on the inspection performance and low cost requirements make the algorithm design a challenging task that requires new and innovative methodologies in image processing and fusion. In this research, an integrated approach that combines novel image processing and fusion techniques is proposed for the efficient design of accurate and real-time machine vision-based inspection algorithms with an application to the food processing problem. Firstly, a general methodology is introduced for effective detection of defects and foreign objects that possess certain spectral and shape features. The factors that affect performance metrics are analyzed, and a recursive segmentation and classification scheme is proposed in order to improve the segmentation accuracy. The developed methodology is applied to real-time fan bone detection in deboned poultry meat with a detection rate of 93% and a false alarm rate of 7% from a lab-scale testing on 280 samples. Secondly, a novel snake-based algorithm is developed for the segmentation of vector-valued images. The snakes are driven by the weighted sum of the optimal forces derived from corresponding energy functionals in each image, where the weights are determined based on a novel metric that measures both local contrasts and noise powers in individual sensor images. This algorithm is effective in improving the segmentation accuracy when imagery from multiple sensors is available to the inspection system. The effectiveness of the developed algorithm is verified using (i) synthesized images (ii) real medical and aerial images and (iii) color and x-ray chicken breast images. The results further confirmed that the algorithm yields higher segmentation accuracy than monosensory methods and is able to accommodate a certain amount of registration error. This feature-level image fusion technique can be combined with pixel- and decision- level techniques to improve the overall inspection system performance.

We propose a novel approach for segmentation of human objects, including face and body, in image sequences. In modern video coding techniques, e.g., MPEG-4 and MPEG-7, human objects are usually the main focus for multimedia applications. We combine temporal and spatial information and employ a neuro-fuzzy mechanism to extract human objects. A fuzzy self-clustering technique is used to divide the video frame into a set of segments. The existence of a face within a candidate face region is ensured by searching for possible constellations of eye-mouth triangles and verifying each eye-mouth combination with the predefined template. Then rough foreground and background are formed based on a combination of multiple criteria. Finally, human objects in the base frame and the remaining frames of the video stream are precisely located by a fuzzy neural network which is trained by a SVD-based hybrid learning algorithm. Through experiments, we compare our system with two other approaches, and the results have shown that our system can detect face locations and extract human objects more accurately.