Morfometría craneofacial sobre ficheros DICOM. Aplicaciones en ciencias morfologicas, forenses y en cirugía reparadora

Three-dimensional Shaping is the subject of ongoing study in disciplines like Anatomy and Anthropology. Not only does it characterize any specimen, allowing identification, but also expresses the genome, resulting in great interest in fields like Paleontology or in the diagnosis of genetic diseases. A traditional Euclidean morphometry has recently been complemented with another revolutionary technique: Geometric Morphometrics, which is able to calculate average three-dimensional shpaes (consensus) and study the variability of other subjects in relation to this one. In addition, diagnostic imaging has transformed medicine, acquiring and storing body regions. This thesis seeks to provide methodology to facilitate the implementation of the Geometric Morphometrics in Medicine, establishing the usefulness of these techniques, and comparing them. Rather than answering all the questions, it opens doors to knowledge of craniofacial morpho-geometric analysis, offering the possibility of a new two-way transfer between the clinical and morphological sciences. METHOD: High and low resolution 190 computed tomography (CT), in two series were used. Landmarks and semi-landmarks were digitized in two and three dimensions. Procrustres overlapping, main component analysis, canonical variate studies, analysis of multiple Euclidean distances, Fourier analysis, thin plate analysis, discriminant function analysis and multiple regression including geometric morphometric methods were used. The results provide, valuable data for human sex dimorphism, cranial variations with age and modularity alometry dimorphism. Geometric Morphometrics different techniques are compared and the most appropriate ones for estimating each of the parameters are listed. CONCLUSIONS DICOM databases are useful to investigate craniofacial 3D shape. Geometric Morphometrics is suitable for this purpose. Data on modern populations and future evolution of skull shape are provided. A methodological analysis and step-by-step walkthrough is provided to choose the appropriate technique for each purpose. One application of these procedures is the estimate of sexual dimorphism and the influence of age in cranial morphology, beyond the stage of development of the skull. Analysis of intracranial skull base landmarks adds additional information to the previously available. Our database provides references for the study of fragmented skulls and has clinical utility, for example, in reconstructive surgery

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

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