High performance computing for 3D image segmentation

Digital image processing is a very popular and still very promising eld of science, which has been successfully applied to numerous areas and problems, reaching elds like forensic analysis, security systems, multimedia processing, aerospace, automotive, and many more. A very important part of the image processing area is image segmentation. This refers to the task of partitioning a given image into multiple regions and is typically used to locate and mark objects and boundaries in input scenes. After segmentation the image represents a set of data far more suitable for further algorithmic processing and decision making. Image segmentation algorithms are a very broad eld and they have received signi cant amount of research interest A good example of an area, in which image processing plays a constantly growing role, is the eld of medical solutions. The expectations and demands that are presented in this branch of science are very high and dif cult to meet for the applied technology. The problems are challenging and the potential bene ts are signi cant and clearly visible. For over thirty years image processing has been applied to different problems and questions in medicine and the practitioners have exploited the rich possibilities that it offered. As a result, the eld of medicine has seen signi cant improvements in the interpretation of examined medical data. Clearly, the medical knowledge has also evolved signi cantly over these years, as well as the medical equipment that serves doctors and researchers. Also the common computer hardware, which is present at homes, of ces and laboratories, is constantly evolving and changing. All of these factors have sculptured the shape of modern image processing techniques and established in which ways it is currently used and developed. Modern medical image processing is centered around 3D images with high spatial and temporal resolution, which can bring a tremendous amount of data for medical practitioners. Processing of such large sets of data is not an easy task, requiring high computational power. Furthermore, in present times the computational power is not as easily available as in recent years, as the growth of possibilities of a single processing unit is very limited - a trend towards multi-unit processing and parallelization of the workload is clearly visible. Therefore, in order to continue the development of more complex and more advanced image processing techniques, a new direction is necessary. A very interesting family of image segmentation algorithms, which has been gaining a lot of focus in the last three decades, is called Deformable Models. They are based on the concept of placing a geometrical object in the scene of interest and deforming it until it assumes the shape of objects of interest. This process is usually guided by several forces, which originate in mathematical functions, features of the input images and other constraints of the deformation process, like object curvature or continuity. A range of very desired features of Deformable Models include their high capability for customization and specialization for different tasks and also extensibility with various approaches for prior knowledge incorporation. This set of characteristics makes Deformable Models a very ef cient approach, which is capable of delivering results in competitive times and with very good quality of segmentation, robust to noisy and incomplete data. However, despite the large amount of work carried out in this area, Deformable Models still suffer from a number of drawbacks. Those that have been gaining the most focus are e.g. sensitivity to the initial position and shape of the model, sensitivity to noise in the input images and to awed input data, or the need for user supervision over the process. The work described in this thesis aims at addressing the problems of modern image segmentation, which has raised from the combination of above-mentioned factors: the signi cant growth of image volumes sizes, the growth of complexity of image processing algorithms, coupled with the change in processor development and turn towards multi-processing units instead of growing bus speeds and the number of operations per second of a single processing unit. We present our innovative model for 3D image segmentation, called the The Whole Mesh Deformation model, which holds a set of very desired features that successfully address the above-mentioned requirements. Our model has been designed speci cally for execution on parallel architectures and with the purpose of working well with very large 3D images that are created by modern medical acquisition devices. Our solution is based on Deformable Models and is characterized by a very effective and precise segmentation capability. The proposed Whole Mesh Deformation (WMD) model uses a 3D mesh instead of a contour or a surface to represent the segmented shapes of interest, which allows exploiting more information in the image and obtaining results in shorter times. The model offers a very good ability for topology changes and allows effective parallelization of work ow, which makes it a very good choice for large data-sets. In this thesis we present a precise model description, followed by experiments on arti cial images and real medical data

The whole mesh Deformation Model for 2D and 3D image segmentation

In this paper we present a novel approach for image segmentation using Active Nets and Active Volumes. Those solutions are based on the Deformable Models, with slight difference in the method for describing the shapes of interests - instead of using a contour or a surface they represented the segmented objects with a mesh structure, which allows to describe not only the surface of the objects but also to model their interiors. This is obtained by dividing the nodes of the mesh in two categories, namely internal and external ones, which will be responsible for two different tasks. In our new approach we propose to negate this separation and use only one type of nodes. Using that assumption we manage to significantly shorten the time of segmentation while maintaining its quality

Green Jackets in Men\u27s Sizes Only: Gender Discrimination at Private Country Clubs

On November 3, 2009, the Supreme Court of Ireland held that the Portmarnock Golf Club could maintain its rule prohibiting female membership free from the sanctions of Ireland\u27s antidiscrimination laws. Portmarnock is representative of the numerous private golf clubs that continue to promote discrimination against women. Despite significant advances in gender equality, private country clubs in the United States, the United Kingdom, and Ireland remain bastions of codified gender discrimination. Many of the most prominent golf clubs hold firmly to discriminatory policies established generations ago. Opposition to these policies has come in various forms of protest and litigation, with mixed results. The private clubs have frequently asserted the right to free and exclusive association to defend their actions. Moreover, some of golf\u27s most famous private clubs continue to practice egregious forms of discrimination against women largely free from legal challenges. This Note examines the existing legal status of gender discrimination at private country clubs in the United States, the United Kingdom, and Ireland and offers a three-prong approach to litigation against clubs engaging in disparate treatment of women

Mid-term evaluation of the support to strengthened bilateral relations under the EEA and Norway Grants

Through the EEA Grants and Norway Grants, Norway, Iceland and Liechtenstein aim to reduce economic and social disparities and strengthen cooperation with 16 countries in Central and Southern Europe. A mid-term evaluation of the current EEA and Norway Grants 2009-14 was conducted by COWI during the second half of 2015 and early 2016 at the request of the Financial Mechanism Office, EEA and Norway Grants. The aim of the mid-term evaluation is to assess to what extent and in which way the EEA and Norway Grants contribute towards strengthening bilateral relations between donor and beneficiary states. The evaluation covers four out of the ten priority sectors of the EEA and Norway Grants and five of the 16 beneficiary countries (Estonia, Latvia, Poland, Romania and Slovakia), representing 19.4% of the allocated total of EUR 1.8 billion

Application of video processing methods for linguistic research

Evolution and changes of all modern languages is a well-known fact. However, recently it is reaching dynamics never seen before, which results in loss of the vast amount of information encoded in every language. In order to preserve such heritage, properly annotated recordings of world languages are necessary. Since creating those annotations is a very laborious task, reaching times 100 longer than the length of the annotated media, innovative video processing algorithms are needed, in order to improve the efficiency and quality of annotation process

Linguistic concepts described with Media Query Language for automated annotation

Introduction Human spoken communication is multimodal, i.e. it encompasses both speech and gesture. Acoustic properties of voice, body movements, facial expression, etc. are an inherent and meaningful part of spoken interaction; they can provide attitudinal, grammatical and semantic information. In the recent years interest in audio-visual corpora has been rising rapidly as they enable investigation of different communicative modalities and provide more holistic view on communication (Kipp et al. 2009). Moreover, for some languages such corpora are the only available resource, as is the case for endangered languages for which no written resources exist

Extended whole mesh deformation model: Full 3D processing

Processing medical data has always been an interesting field that has shown the need for effective image segmentation methods. Modern medical image segmentation solutions are focused on 3D image volumes, which originate at advanced acquisition devices. Operating on such data in a 3D envi- ronment is essential in order to take the full advantage of the available information. In this paper we present an extended version of our 3D image segmentation and reconstruction model that belongs to the family of Deformable Models and is capable of processing large image volumes in competitive times and in fully 3D environment, offering a big level of automation of the process and a high precision of results. It is also capable of handling topology changes and offers a very good scalability on multi-processing unit architectures. We present a description of the model and show its capabilities in the field of medical image processing

Julkisen hallinnon kriisinhallinta: Joustavuuteen ja kommunikaatioon perustuva organisaatiotoiminta sekä kriisijohtajan kompetenssit

Yhteiskunnallisten kriisien yleistyessä sekä niiden vaikutusten laajentuessa kriisinhallinnalliset toimet ovat kokeneet murroksen, jossa varautumista ja ennaltaehkäisemistä vaaditaan aiempaa enemmän. Globalisaatio ja sen kautta rakentuneet moninaiset organisaatioverkostot ovat osaltaan vauhdittaneet kriisien mahdollisuutta levitä yhteiskunnallisiin rakenteisiin. Julkisen hallinnon ennaltaehkäisemiskeinot muodostuvat kahdesta teoreettisesta kokonaisuudesta; kriisijohtajan kompetensseista sekä julkisen organisaation turvallisuuskulttuurillisesta toiminnasta. Lähtökohtaisesti organisaatiossa tapahtuva kriisitoiminta nojaa yksittäisten kriisijohtajien toimintaan sekä päätöksentekoon. Kriisijohtajan kompetenssin perustan muodostaa henkilökohtaiset ominaisuudet ja taidot sekä käyttäytymismallit. Tutkielmassa organisaation ennaltaehkäisemistä tarkastellaan teoriaosuudessa kolmen teoreettisen kokonaisuuden kautta; organisaation kompetenssi, -joustavuus sekä -riskiymmärrys. Nämä kolme kokonaisuutta perustuvat organisaatiossa toteutettavaan kokonaisjoustavuuteen sekä -kommunikaatioon, joiden kautta varautumista ja ennaltaehkäisemistä suunnitellaan sekä toteutetaan. Tutkimuksen menetelmänä toimii laadullinen tutkimus, jossa tutkimusaineisto hankittiin puolistrukturoitujen teemahaastattelujen kautta. Tutkimusaineistoa hankittiin yhdeksältä johtajalta, jotka työskentelevät julkisen hallinnon varautumisen piirissä. Haastateltaviin sisältyi varautumispäällikkö, turvallisuuspäälliköitä, kaupunginjohtaja sekä ylijohtaja. Aineiston analyysissä hyödynnettiin sisällönanalyysiä. Tutkimustulosten mukaan julkisen hallinnon ennaltaehkäiseminen perustuu valmiuteen keskittyvään suunnitteluun, jossa pyritään tunnistamaan organisaatioympäristössä esiintyviä signaaleja mahdollisista kriiseistä. Tutkimustuloksissa painotettiin ennaltaehkäisemisen vaativan myös jatkuvaa työtä, jossa organisaation sisäinen, mutta myös ulkoinen kommunikaatio ja viestintä ovat avainasemassa. Modernit kriisit vaativat myös entistä enemmän joustavuutta, jonka kautta kriisin synnyttämiin muutospaineisiin voidaan vastata mahdollisimman tehokkaasti. As social crises become more common and their impacts broaden, crisis management has undergone a transformation, in which preparedness and prevention are now required more than ever. Globalization and the various organizational networks built through it have contributed to the possibility of crises spreading to social structures. The preventive measures of public administration consist of two theoretical entities: the competencies of crisis managers and the safety culture of public organizations. Essentially, crisis management within an organization relies on the actions and decision-making of individual crisis managers. The foundation of a crisis manager's competency is based on their personal traits and skills, as well as their behavior patterns. In the theoretical section of this thesis, organizational prevention is examined through three theoretical entities: organizational competency, flexibility, and risk awareness. These three entities are based on overall flexibility and communication within the organization, through which preparedness and prevention are planned and implemented. Qualitative research is used as the methodology in this study, with research material obtained through semi-structured theme interviews. The research material was obtained from nine managers working in the field of public administration preparedness, including a preparedness manager, security managers, a city manager, and a chief executive officer. Content analysis was utilized in the analysis of the material. The research findings indicate that public administration prevention is based on readiness-focused planning, where efforts are made to identify signals of potential crises within the organizational environment. The research results emphasized that prevention also requires ongoing work, where both internal and external communication and messaging within the organization are crucial. Modern crises also require more flexibility, through which the pressures for change created by a crisis can be responded to as effectively as possible

The whole mesh deformation model: A fast image segmentation method suitable for effective parallelization

In this article, we propose a novel image segmentation method called the whole mesh deformation (WMD) model, which aims at addressing the problems of modern medical imaging. Such problems have raised from the combination of several factors: (1) significant growth of medical image volumes sizes due to increasing capabilities of medical acquisition devices; (2) the will to increase the complexity of image processing algorithms in order to explore new functionality; (3) change in processor development and turn towards multi processing units instead of growing bus speeds and the number of operations per second of a single processing unit. Our solution is based on the concept of deformable models and is characterized by a very effective and precise segmentation capability. The proposed WMD model uses a volumetric mesh instead of a contour or a surface to represent the segmented shapes of interest, which allows exploiting more information in the image and obtaining results in shorter times, independently of image contents. The model also offers a good ability for topology changes and allows effective parallelization of workflow, which makes it a very good choice for large datasets. We present a precise model description, followed by experiments on artificial images and real medical data