Image thinning by hexagonal grid

One of the tasks of image processing systems is to characterize images by thinning, or skeletonizing techniques. The standard method, based on a disk growing technique, often results in disconnected skeletons. Since the standard technique is based on a square grid system, the disk shape is also square. The. purpose of this research has been to determine whether or not a hex based grid will provide better connected skeletons for images originally represented in a square grid system. The algorithms to implement this project have been implemented in the X-Window environment and also make use of Dataparallel C

Automatic Main Road Extraction from High Resolution Satellite Imagery

Road information is essential for automatic GIS (geographical information system) data acquisition, transportation and urban planning. Automatic road (network) detection from high resolution satellite imagery will hold great potential for significant reduction of database development/updating cost and turnaround time. From so called low level feature detection to high level context supported grouping, so many algorithms and methodologies have been presented for this purpose. There is not any practical system that can fully automatically extract road network from space imagery for the purpose of automatic mapping. This paper presents the methodology of automatic main road detection from high resolution satellite IKONOS imagery. The strategies include multiresolution or image pyramid method, Gaussian blurring and the line finder using 1-dimemsional template correlation filter, line segment grouping and multi-layer result integration. Multi-layer or multi-resolution method for road extraction is a very effective strategy to save processing time and improve robustness. To realize the strategy, the original IKONOS image is compressed into different corresponding image resolution so that an image pyramid is generated; after that the line finder of 1-dimemsional template correlation filter after Gaussian blurring filtering is applied to detect the road centerline. Extracted centerline segments belong to or do not belong to roads. There are two ways to identify the attributes of the segments, the one is using segment grouping to form longer line segments and assign a possibility to the segment depending on the length and other geometric and photometric attribute of the segment, for example the longer segment means bigger possibility of being road. Perceptual-grouping based method is used for road segment linking by a possibility model that takes multi-information into account; here the clues existing in the gaps are considered. Another way to identify the segments is feature detection back-to-higher resolution layer from the image pyramid

Topological analysis of the tumour microenvironment to study Neuroblastoma

Solid tumours and their tumour microenvironment (TME) can be considered as complex networks whose elements are in constant physical stress. All the elements of the TME, including tumour cells, stromal cells, immune and stem cells, blood/lymphatic vessels, nerve fibers and extracellular matrix components, belong to a highly balanced compressiontension molecular and cellular structure. Through mechanical signals, each element could affect its surroundings modulating tumour growth and migration. The analysis of these complex interactions and the understanding of the structural organization of a tumour requires the collaboration of different disciplines. In this thesis, we focus on a particular solid tumour: Neuroblastoma, a rare type of cancer, originated during the embryo development. We apply computational and mathematical tools to analyse the topology of vitronectin, a glycoprotein of the extracellular matrix, in neuroblastoma tumours. Vitronectin has a particular interest in tumour biology where it is associated with cell migration, angiogenesis, and matrix degradation. Still, its role in Neuroblastoma is not clear. Here, we study the organization of vitronectin within the TME considering Neuroblastoma patient prognosis and tumoral aggressiveness. Combing graph theory and image analysis, we characterize histopathological images taken, from a human sample, by analysing different topological features that capture the organizational cues of vitronectin. By means of statistical analyses, we find that two topological features (Euler number and branching), related to the organization of the existing vitronectin within and surrounding the cells (territorial), correlates with risk pre-stratification group and genetic instability criterion. We interpret that a large amount of recently synthesized VN would create tracks to aid malignant neuroblasts to invade other organs, pinpointed by both topological features, which in turn would change, dramatically, the constitution and mechanics of the extracellular matrix, increasing tumour aggressiveness and worsen patient outcomes. Further studies will be required to assess the true potential of vitronectin as a future therapeutic target of neuroblastoma.Los tumores sólidos y su microambiente tumoral (TME) pueden ser vistos como redes complejas cuyos elementos están en constante estrés físico. Todos los elementos del TME, incluidas células tumorales, células del estroma, células inmunes y células troncales, vasos sanguíneos o linfáticos, fibras nerviosas y componentes de la matriz extracelular, pertenecen a una maquinaria molecular y celular de tensión-compresión altamente equilibrada. A través de señales mecánicas, cada elemento podría afectar su entorno modulando el crecimiento tumoral y la migración. El análisis de estas interacciones complejas y la comprensión de la organización estructural de un tumor requiere la colaboración de diferentes disciplinas. En esta tesis, nos centramos en un tumor sólido particular: el neuroblastoma, un cáncer considerado como ‘raro’, que se origina durante el desarrollo del embrión. Aplicando herramientas computacionales y matemáticas, analizamos la topología de la vitronectina, una glicoproteína de la matriz extracelular, en tumores de neuroblastoma. La vitronectina tiene un interés particular en la biología tumoral, ya que está asociada con migración celular, angiogénesis y degradación de la propia matriz. Aún así, su papel en el neuroblastoma no está claro. En este trabajo, estudiamos la organización de la vitronectina dentro del microambiente tumoral, considerando el pronóstico del paciente con neuroblastoma y su agresividad tumoral. Combinando la teoría de gráficos y el análisis de imagen, caracterizamos las imágenes histopatológicas tomadas de una muestra humana, mediante el análisis de diferentes características topológicas que capturan la organización de la vitronectina. Mediante análisis estadísticos, encontramos que dos características topológicas (número de Euler y ‘ramificación’), relacionadas con la organización de la vitronectina existente dentro y alrededor de las células (territorial), se correlacionan con el grupo de pre-estratificación de riesgo y la inestabilidad genética del paciente. En consecuencia, interpretamos que una gran cantidad de VN, sintetizada recientemente, crearía una especia de ‘caminos’ para ayudar a los neuroblastos malignos a invadir otros órganos, que a su vez cambiarían dramáticamente la constitución y la mecánica de la matriz extracelular, aumentando la agresividad del tumor y empeorando el pronóstico del paciente. Futuros estudios serán requeridos para evaluar el verdadero potencial de la vitronectina como una diana terapéutica del neuroblastoma a largo plazo

High Resolution Maps of the Vasculature of An Entire Organ

The structure of vascular networks represents a great, unsolved problem in anatomy. Network geometry and topology differ dramatically from left to right and person to person as evidenced by the superficial venation of the hands and the vasculature of the retinae. Mathematically, we may state that there is no conserved topology in vascular networks. Efficiency demands that these networks be regular on a statistical level and perhaps optimal. We have taken the first steps towards elucidating the principles underlying vascular organization, creating the rst map of the hierarchical vasculature (above the capillaries) of an entire organ. Using serial blockface microscopy and fluorescence imaging, we are able to identify vasculature at 5 μm resolution. We have designed image analysis software to segment, align, and skeletonize the resulting data, yielding a map of the individual vessels. We transformed these data into a mathematical graph, allowing computationally efficient storage and the calculation of geometric and topological statistics for the network. Our data revealed a complexity of structure unexpected by theory. We observe loops at all scales that complicate the assignment of hierarchy within the network and the existence of set length scales, implying a distinctly non-fractal structure of components within

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Regular Hierarchical Surface Models: A conceptual model of scale variation in a GIS and its application to hydrological geomorphometry

Environmental and geographical process models inevitably involve parameters that vary spatially. One example is hydrological modelling, where parameters derived from the shape of the ground such as flow direction and flow accumulation are used to describe the spatial complexity of drainage networks. One way of handling such parameters is by using a Digital Elevation Model (DEM), such modelling is the basis of the science of geomorphometry. A frequently ignored but inescapable challenge when modellers work with DEMs is the effect of scale and geometry on the model outputs. Many parameters vary with scale as much as they vary with position. Modelling variability with scale is necessary to simplify and generalise surfaces, and desirable to accurately reconcile model components that are measured at different scales. This thesis develops a surface model that is optimised to represent scale in environmental models. A Regular Hierarchical Surface Model (RHSM) is developed that employs a regular tessellation of space and scale that forms a self-similar regular hierarchy, and incorporates Level Of Detail (LOD) ideas from computer graphics. Following convention from systems science, the proposed model is described in its conceptual, mathematical, and computational forms. The RHSM development was informed by a categorisation of Geographical Information Science (GISc) surfaces within a cohesive framework of geometry, structure, interpolation, and data model. The positioning of the RHSM within this broader framework made it easier to adapt algorithms designed for other surface models to conform to the new model. The RHSM has an implicit data model that utilises a variation of Middleton and Sivaswamy (2001)’s intrinsically hierarchical Hexagonal Image Processing referencing system, which is here generalised for rectangular and triangular geometries. The RHSM provides a simple framework to form a pyramid of coarser values in a process characterised as a scaling function. In addition, variable density realisations of the hierarchical representation can be generated by defining an error value and decision rule to select the coarsest appropriate scale for a given region to satisfy the modeller’s intentions. The RHSM is assessed using adaptions of the geomorphometric algorithms flow direction and flow accumulation. The effects of scale and geometry on the anistropy and accuracy of model results are analysed on dispersive and concentrative cones, and Light Detection And Ranging (LiDAR) derived surfaces of the urban area of Dunedin, New Zealand. The RHSM modelling process revealed aspects of the algorithms not obvious within a single geometry, such as, the influence of node geometry on flow direction results, and a conceptual weakness of flow accumulation algorithms on dispersive surfaces that causes asymmetrical results. In addition, comparison of algorithm behaviour between geometries undermined the hypothesis that variance of cell cross section with direction is important for conversion of cell accumulations to point values. The ability to analyse algorithms for scale and geometry and adapt algorithms within a cohesive conceptual framework offers deeper insight into algorithm behaviour than previously achieved. The deconstruction of algorithms into geometry neutral forms and the application of scaling functions are important contributions to the understanding of spatial parameters within GISc

Local Geometric Transformations in Image Analysis

The characterization of images by geometric features facilitates the precise analysis of the structures found in biological micrographs such as cells, proteins, or tissues. In this thesis, we study image representations that are adapted to local geometric transformations such as rotation, translation, and scaling, with a special emphasis on wavelet representations. In the first part of the thesis, our main interest is in the analysis of directional patterns and the estimation of their location and orientation. We explore steerable representations that correspond to the notion of rotation. Contrarily to classical pattern matching techniques, they have no need for an a priori discretization of the angle and for matching the filter to the image at each discretized direction. Instead, it is sufficient to apply the filtering only once. Then, the rotated filter for any arbitrary angle can be determined by a systematic and linear transformation of the initial filter. We derive the Cramér-Rao bounds for steerable filters. They allow us to select the best harmonics for the design of steerable detectors and to identify their optimal radial profile. We propose several ways to construct optimal representations and to build powerful and effective detector schemes; in particular, junctions of coinciding branches with local orientations. The basic idea of local transformability and the general principles that we utilize to design steerable wavelets can be applied to other geometric transformations. Accordingly, in the second part, we extend our framework to other transformation groups, with a particular interest in scaling. To construct representations in tune with a notion of local scale, we identify the possible solutions for scalable functions and give specific criteria for their applicability to wavelet schemes. Finally, we propose discrete wavelet frames that approximate a continuous wavelet transform. Based on these results, we present a novel wavelet-based image-analysis software that provides a fast and automatic detection of circular patterns, combined with a precise estimation of their size

Novel developments of Moiré techniques for industrial applications.

The family of moire and fringe projection techniques can be used to measure the shape, orientation and deformation of arbitrary objects. These experimental techniques are easy to automate, allow remote operation, provide full-field information and are versatile, inexpensive and relatively simple. They have been applied extensively in the past, but mostly in the controlled environment of a laboratory. There is great potential in the use of these techniques for a variety of industrial applications including quality control and process monitoring. However, this implies dealing with the adverse conditions of the factory, hangar or similar environment. In addition, these techniques will only appeal to industry if they are fast, simple, and foolproof. The main goal of this research was to exploit recent technological advances to fulfil the requirements of industry, making these techniques easier to use and more robust, and explore the potential offered by the combination and cross-fertilization of moire methods with techniques from different fields such as experimental stress analysis, non-destructive evaluation, and machine vision. This research resulted in the development of a number of instruments and procedures for industrial applications based in moire and fringe projection techniques, including: • A handheld instrument based in the shadow moire technique designed to assist in the detection of very small surface defects in aircraft parts, during in-service maintenance inspections; • A multi-purpose system to measure remotely (i) the shape and deformation of three dimensional objects by means of the fringe projection technique, and (ii) the location of the object by means of triangulation. The elements were integrated in a portable instrument, and fully automated novel algorithms were implemented to process the data; • Finally, a novel experimental technique is proposed that uses thermal marking to measure deformation in a component, in a combination of concepts from moire and thermography. Experimental results obtained in a range of situations are presented in several industrial applications in the context of the aerospace industry and in bioengineering