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http://hdl.handle.net/2445/59825
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DC Field | Value | Language |
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dc.contributor.advisor | Igual Muñoz, Laura | - |
dc.contributor.author | Hinarejos Gimenez, Andreu | - |
dc.date.accessioned | 2014-11-20T08:21:01Z | - |
dc.date.available | 2014-11-20T08:21:01Z | - |
dc.date.issued | 2014-06-20 | - |
dc.identifier.uri | http://hdl.handle.net/2445/59825 | - |
dc.description | Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2014, Director: Laura Igual Muñoz | ca |
dc.description.abstract | An exponential improvement in computation capacity throughout the last decades has allowed the use of further computationally demanding algorithms to solve many kinds of problems in real time. In this project both computer vision and machine learning techniques are applied to support a neuroimage study. In particular, a fully automatic method to segment the caudate nucleus of the brain from a magnetic resonance image (MRI). Studies have shown that neuroanatomical abnormalities in the caudate nucleus are strongly related to pediatric attentiondeficit/hiperactivity disorders (ADHD). Therefore, providing an automatic subjectiveless tool to segment its volume not only improves the diagnose, but it also speeds up the process, freeing the experts from the arduous segmenting task. In order to achieve this purpose Graphcut unsupervised was developed. In this project a supervised term is added to the currently existing envoirment. Particularly a support vector machine classifier is trained with a set of MRI slices, which is used to refine the segmentation algorithm results. | ca |
dc.format.extent | 44 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | spa | ca |
dc.rights | memòria: cc-by-sa (c) Andreu Hinarejos Gimenez, 2014 | - |
dc.rights | codi: GPL (c) Andreu Hinarejos Gimenez, 2014 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/3.0/es | - |
dc.rights.uri | http://www.gnu.org/licenses/gpl-3.0.ca.html | - |
dc.source | Treballs Finals de Grau (TFG) - Enginyeria Informàtica | - |
dc.subject.classification | Algorismes computacionals | cat |
dc.subject.classification | Visió per ordinador | cat |
dc.subject.classification | Programari | cat |
dc.subject.classification | Treballs de fi de grau | cat |
dc.subject.classification | Aprenentatge automàtic | ca |
dc.subject.classification | Imatges per ressonància magnètica | ca |
dc.subject.other | Computer algorithms | eng |
dc.subject.other | Computer vision | eng |
dc.subject.other | Computer software | eng |
dc.subject.other | Bachelor's theses | eng |
dc.subject.other | Machine learning | eng |
dc.subject.other | Magnetic resonance imaging | eng |
dc.title | Algoritmo supervisado de segmentación automática para imágenes de resonancia magnética | ca |
dc.type | info:eu-repo/semantics/bachelorThesis | ca |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca |
Appears in Collections: | Programari - Treballs de l'alumnat Treballs Finals de Grau (TFG) - Enginyeria Informàtica |
Files in This Item:
File | Description | Size | Format | |
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memoria.pdf | Memòria | 1.86 MB | Adobe PDF | View/Open |
codi_font.zip | Codi font | 65.22 MB | zip | View/Open |
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