Pattern recognition approaches for biomedical data in computer-assisted cancer research

Gómez, Juan Miguel

doi:10.4995/thesis/10251/4602

Cited by 2 publications

(3 citation statements)

References 146 publications

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“…To address the latter classification problem, the metabolite ratios of the spectra were also used. The evaluation results of the different García-Gómez et al [41] eTUMOUR Low grade glioma vs high grade tumor 92 84 92 1.5 Tesla MRS data of Philips (sdat/spar) GE up to 9X (SAGE Pxxxx with an shf or sdf/shf) siemens scanners (numaris 4) jMRUI [58] …”

Section: Voxel Assignment Accuracymentioning

confidence: 99%

“…Furthermore, the design of the DSS provided a comparative analysis with the average spectra of 12 standard brain tumor types of an unknown brain tumor. During the classification procedure the assigned class as well as the posterior probabilities of each class were displayed to the system [39,41] . HealthAgents HealthAgents [42] was a distributed DSS (d-DSS) built upon INTERPRET and eTUMOUR projects.…”

Section: Voxel Assignment Accuracymentioning

confidence: 99%

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Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

Tsolaki

Kousi

Svolos

et al. 2014

WJR

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In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and noninvasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

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Section: Voxel Assignment Accuracymentioning

confidence: 99%

Section: Voxel Assignment Accuracymentioning

confidence: 99%

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

Tsolaki

Kousi

Svolos

et al. 2014

WJR

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“…No entanto, existe uma diferença entre o conteúdo visual (percepção humana) e a representação digital (numérica) de uma imagem (representação computarizada), esse problema é chamado de gap semântico. Como solução para lidar com o gap semântico, foram desenvolvidos extratores de características específicos para a área médica (García-Gómez, 2009;Ghosh et al, 2011). Por outro lado, o funcionamento 6 2.1.…”

Section: Conceitos Necessários Para Recuperação De Imagens Baseada Emunclassified

Seleção supervisionada de características por ranking para processar consultas por similaridade em imagens médicas

Mamani¹

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You cannot say exactly the same thing in two different ways. Slightly alter the expression, and you slightly alter the idea".-Arnold Bennett v Agradecimentos À minha orientadora, Profa. Dra. Agma J. M. Traina pelo apoio, paciência, sabedoria e especialmente pelos conselhos que me apresentou para tornar possível esse trabalho. Aos meus pais, "Bertita" e Fermin, e irmãos Saúl, Maritza e Elsa pelo incessante incentivo e apoio nesta etapa na minha vida. Às minhas sobrinhas Alondrita e Kami, e às minhas sobrinhas e sobrinhos, que estão por chegar e que com certeza trarão muita alegria na minha Família. Aos meus amigos Fernando e Jorge "conchán", e em especial a Karina por ter me ensinado muito nesta vida e pelos gratos momentos que passamos. Aos meus amigos da "Republica.PE". Aos meus colegas, do ICMC e do GBdI que me apoiaram ao longo do mestrado. Agradecimentos especiais ao Alceu, Daniel Mario, Felipe, Newton e a Ândrea que ajudaram muito com a revisão desta dissertação.

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Pattern recognition approaches for biomedical data in computer-assisted cancer research

Cited by 2 publications

References 146 publications

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

Seleção supervisionada de características por ranking para processar consultas por similaridade em imagens médicas

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