331 research outputs found

    Keyword and Image Content Features for Image Indexing and Retrieval Within Compressed Domain

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    The central problem of most Content Based Image Retrieval approaches is poor quality in terms of sensitivity (recall) and specificity (precision). To overcome this problem, the semantic gap between high-level concepts and low-level features has been acknowledged. In this paper we introduce an approach to reduce the impact of the semantic gap by integrating high-level (semantic) and low-level features to improve the quality of Image Retrieval queries. Our experiments have been carried out by applying two hierarchical procedures. The first approach is called keyword-content, and the second content-keyword. Our proposed approaches show better results compared to a single method (keyword or content based) in term of recall and precision. The average precision has increased by up to 50%

    Data and knowledge engineering for medical image and sensor data

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    KEYWORD AND IMAGE CONTENT FEATURES FOR IMAGE INDEXING AND RETRIEVAL WITHIN COMPRESSED DOMAIN

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    The central problem of most Content Based Image Retrieval approaches is poor quality in terms of sensitivity (recall) and specificity (precision). To overcome this problem, the semantic gap between high-level concepts and low-level features has been acknowledged. In this paper we introduce an approach to reduce the impact of the semantic gap by integrating high-level (semantic) and low-level features to improve the quality of Image Retrieval queries. Our experiments have been carried out by applying two hierarchical procedures. The first approach is called keyword-content, and the second content-keyword. Our proposed approaches show better results compared to a single method (keyword or content based) in term of recall and precision. The average precision has increased by up to 50%

    Medical imaging analysis with artificial neural networks

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    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

    Acute Angle Repositioning in Mobile C-Arm Using Image Processing and Deep Learning

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    During surgery, medical practitioners rely on the mobile C-Arm medical x-ray system (C-Arm) and its fluoroscopic functions to not only perform the surgery but also validate the outcome. Currently, technicians reposition the C-Arm arbitrarily through estimation and guesswork. In cases when the positioning and repositioning of the C-Arm are critical for surgical assessment, uncertainties in the angular position of the C-Arm components hinder surgical performance. This thesis proposes an integrated approach to automatically reposition C-Arms during critically acute movements in orthopedic surgery. Robot vision and control with deep learning are used to determine the necessary angles of rotation for desired C-Arm repositioning. More specifically, a convolutional neural network is trained to detect and classify internal bodily structures. Image generation using the fast Fourier transform and Monte Carlo simulation is included to improve the robustness of the training progression of the neural network. Matching control points between a reference x-ray image and a test x-ray image allows for the determination of the projective transformation relating the images. From the projective transformation matrix, the tilt and orbital angles of rotation of the C-Arm are calculated. Key results indicate that the proposed method is successful in repositioning mobile C-Arms to a desired position within 8.9% error for the tilt and 3.5% error for the orbit. As a result, the guesswork entailed in fine C-Arm repositioning is replaced by a better, more refined method. Ultimately, confidence in C-Arm positioning and repositioning is reinforced, and surgical performance with the C-Arm is improved

    A graph-based approach for the retrieval of multi-modality medical images

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    Medical imaging has revolutionised modern medicine and is now an integral aspect of diagnosis and patient monitoring. The development of new imaging devices for a wide variety of clinical cases has spurred an increase in the data volume acquired in hospitals. These large data collections offer opportunities for search-based applications in evidence-based diagnosis, education, and biomedical research. However, conventional search methods that operate upon manual annotations are not feasible for this data volume. Content-based image retrieval (CBIR) is an image search technique that uses automatically derived visual features as search criteria and has demonstrable clinical benefits. However, very few studies have investigated the CBIR of multi-modality medical images, which are making a monumental impact in healthcare, e.g., combined positron emission tomography and computed tomography (PET-CT) for cancer diagnosis. In this thesis, we propose a new graph-based method for the CBIR of multi-modality medical images. We derive a graph representation that emphasises the spatial relationships between modalities by structurally constraining the graph based on image features, e.g., spatial proximity of tumours and organs. We also introduce a graph similarity calculation algorithm that prioritises the relationships between tumours and related organs. To enable effective human interpretation of retrieved multi-modality images, we also present a user interface that displays graph abstractions alongside complex multi-modality images. Our results demonstrated that our method achieved a high precision when retrieving images on the basis of tumour location within organs. The evaluation of our proposed UI design by user surveys revealed that it improved the ability of users to interpret and understand the similarity between retrieved PET-CT images. The work in this thesis advances the state-of-the-art by enabling a novel approach for the retrieval of multi-modality medical images

    Automated visual measurement of body shape in scoliosis.

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    This thesis describes the content and progression of research into automated non-contact methods for measuring the three-dimensional shape of the human back in scoliosis. Scoliosis is a condition in which the spine becomes distorted and a rib-hump appears on the surface of the back. The research was driven by the needs of the scoliosis clinician and was supported by the Royal Liverpool Children's Hospital, Merseyside. A number of optical methods for measuring back surface shape are considered. Moire contouring and Fourier transform profilometry are investigated through practical research in the laboratory. Stereophotogrammetry, phase stepping profilometry, optical scanning and raster pattern contouring are investigated through consideration of theory and literature review. However, none of these approaches is found to be free from limitations. The main novel content of the work presented in this thesis lies in the research into a new method for reconstructing back shape. A new optical method is proposed in which a modified multi-stripe structured light pattern is projected onto the surface of the back. Image processing operations, specialised for this application, process the image of the pattern to reconstruct three-dimensional shape. Further research demonstrates that the computer reconstruction can be interrogated to measure parameters of clinical significance such as Angle of Trunk Inclination and Standardised Trunk Asymmetry Score. A working clinical system was implemented and tested on scoliosis patients at the hospital. The method is evaluated in terms of technical qualities and as a usable clinical tool and was found to satisfy the criteria for a successful automated system

    Three Dimensional Nonlinear Statistical Modeling Framework for Morphological Analysis

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    This dissertation describes a novel three-dimensional (3D) morphometric analysis framework for building statistical shape models and identifying shape differences between populations. This research generalizes the use of anatomical atlases on more complex anatomy as in case of irregular, flat bones, and bones with deformity and irregular bone growth. The foundations for this framework are: 1) Anatomical atlases which allow the creation of homologues anatomical models across populations; 2) Statistical representation for output models in a compact form to capture both local and global shape variation across populations; 3) Shape Analysis using automated 3D landmarking and surface matching. The proposed framework has various applications in clinical, forensic and physical anthropology fields. Extensive research has been published in peer-reviewed image processing, forensic anthropology, physical anthropology, biomedical engineering, and clinical orthopedics conferences and journals. The forthcoming discussion of existing methods for morphometric analysis, including manual and semi-automatic methods, addresses the need for automation of morphometric analysis and statistical atlases. Explanations of these existing methods for the construction of statistical shape models, including benefits and limitations of each method, provide evidence of the necessity for such a novel algorithm. A novel approach was taken to achieve accurate point correspondence in case of irregular and deformed anatomy. This was achieved using a scale space approach to detect prominent scale invariant features. These features were then matched and registered using a novel multi-scale method, utilizing both coordinate data as well as shape descriptors, followed by an overall surface deformation using a new constrained free-form deformation. Applications of output statistical atlases are discussed, including forensic applications for the skull sexing, as well as physical anthropology applications, such as asymmetry in clavicles. Clinical applications in pelvis reconstruction and studying of lumbar kinematics and studying thickness of bone and soft tissue are also discussed
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