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dc.contributor.authorOrtiz-García, Andrés 
dc.contributor.authorLozano, Francisco
dc.contributor.authorPeinado-Domínguez, Alberto 
dc.contributor.authorGarcía, María
dc.date.accessioned2017-07-06T11:55:37Z
dc.date.available2017-07-06T11:55:37Z
dc.date.created2017
dc.date.issued2017-07-06
dc.identifier.urihttp://hdl.handle.net/10630/14137
dc.description.abstractMedical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain rep- resentative features from the images, play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is spe- cially important in the early diagnosis of dementias. In this work we present a technique that allows extracting discriminative features from Positron Emission Tomography (PET) by means of an Empirical Mode Decomposition-based (EEMD) method. This requires to transform the 3D PET image into a time series which is addressed by sampling the image using a fractal-based method which allows to preserve the spa- tial relationship among voxels. The devised technique has been used to classify images from the Alzheimer's Disease Neuroimaging Initiat- ive (ADNI) achieving up to a 90.5% accuracy in a differential diagnosis task (AD vs. controls), which proves that the information retrieved by our methodology is significantly linked to the disease.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isospaes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectMedicina - Procesado de imágeneses_ES
dc.subject.otherPETes_ES
dc.subject.otherClassificationes_ES
dc.subject.otherMedical image processinges_ES
dc.titlePET image classification using HHT-based features through fractal samplinges_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.relation.eventtitleInternational work-conference on the interplay between natural and artificial computationes_ES
dc.relation.eventplaceA Coruñaes_ES
dc.relation.eventdate19/6/2017es_ES
dc.rights.ccby-nc-nd


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