Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/59825
Title: Algoritmo supervisado de segmentación automática para imágenes de resonancia magnética
Author: Hinarejos Gimenez, Andreu
Director: Igual Muñoz, Laura
Keywords: Algorismes computacionals
Visió per ordinador
Programari
Tesis
Aprenentatge automàtic
Imatges per ressonància magnètica
Computer algorithms
Computer vision
Computer software
Theses
Machine learning
Magnetic resonance imaging
Issue Date: 20-Jun-2014
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.
Note: Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2014, Director: Laura Igual Muñoz
URI: http://hdl.handle.net/2445/59825
Appears in Collections:Treballs Finals de Grau (TFG) - Enginyeria Informàtica
Programari - Treballs de l'alumnat

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