Image similarity for registration and manifold learning: application to brain analysis
The aim of this thesis is to explore and develop measures for image comparison in two main areas of medical image analysis: image registration and manifold learning for population analysis. In particular, the main contributions of the thesis are the following: (i) the development of a multimodal sim...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/402101 |
| Acceso en línea: | http://hdl.handle.net/10803/402101 |
| Access Level: | acceso abierto |
| Palabra clave: | Similarity measure Image registration Simultaneous diagonalization Laplacian commutator Manifold learning Manifold embedding quality Kernel combinations Random forest Neighborhood approximation Brain analysis Medido de similitud Registro de imagen Diagonalización simultáneo Comutador de Laplaciano Cualidad de embeddings Combinación de kernels Aproximación de vecindarios Análisis del cerebro 62 |
| Sumario: | The aim of this thesis is to explore and develop measures for image comparison in two main areas of medical image analysis: image registration and manifold learning for population analysis. In particular, the main contributions of the thesis are the following: (i) the development of a multimodal similarity measure using the commutativity of the image graph Laplacians as a criterion of image structure preservation, (ii) the application of such similarity measure to fetal ultrasound-magnetic resonance registration, (iii) the development of a framework for optimal kernel-based manifold embeddings for medical image data, and (iv) the development of a method to learn and combine heterogeneous pairwise image similarities induced by application-specific distance functions for manifold learning. The methods developed in this thesis were evaluated both on synthetic and clinical data, here in particular for brain analysis and classification, both during early childhood and in aging adults. |
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