Advances in Analysis and Exploration in Medical Imaging

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Vigário, Ricardo, Doc., Aalto University, Department of Information and Computer Science, Finland Gonçalves, Nicolau 2014-11-13T10:00:22Z 2014-11-13T10:00:22Z 2014
dc.identifier.isbn 978-952-60-5948-8 (electronic)
dc.identifier.isbn 978-952-60-5947-1 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.description.abstract With an ever increasing life expectancy, we see a concomitant increase in diseases capable of disrupting normal cognitive processes. Their diagnoses are difficult, and occur usually after daily living activities have already been compromised. This dissertation proposes machine learning methods for the study of the neurological implications of brain lesions. It addresses the analysis and exploration of medical imaging data, with particular emphasis to (f)MRI. Two main research directions are proposed. In the first, a brain tissue segmentation approach is detailed. In the second, a document mining framework, applied to reports of neuroscientific studies, is described. Both directions are based on retrieving consistent information from multi-modal data. A contribution in this dissertation is the application of a semi-supervised method, discriminative clustering, to identify different brain tissues and their partial volume information. The proposed method relies on variations of tissue distributions in multi-spectral MRI, and reduces the need for a priori information. This methodology was successfully applied to the study of multiple sclerosis and age related white matter diseases. It was also showed that early-stage changes of normal-appearing brain tissue can already predict decline in certain cognitive processes. Another contribution in this dissertation is in neuroscience meta-research. One limitation in neuroimage processing relates to data availability. Through document mining of neuroscientific reports, using images as source of information, one can harvest research results dealing with brain lesions. The context of such results can be extracted from textual information, allowing for an intelligent categorisation of images. This dissertation proposes new principles, and a combination of several techniques to the study of published fMRI reports. These principles are based on a number of distance measures, to compare various brain activity sites. Application to studies of the default mode network validated the proposed approach. The aforementioned methodologies rely on clustering approaches. When dealing with such strategies, most results depend on the choice of initialisation and parameter settings. By defining distance measures that search for clusters of consistent elements, one can estimate a degree of reliability for each data grouping. In this dissertation, it is shown that such principles can be applied to multiple runs of various clustering algorithms, allowing for a more robust estimation of data agglomeration. en
dc.format.extent 134 + app. 126
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 178/2014
dc.relation.haspart [Publication 1]: Nicolau Gonçalves and Ricardo Vigário. Clustering through SOM Consistency. In Lecture Notes on Computer Science - Image Analysis and Recognition, A. Campilho and M. Kamel (Eds.): Proceedings of the 9th International Conference on Image Analysis and Recognition, ICIAR 2012, pages 61-68, June 2012, DOI 10.1007/978-3-642-31295-3_8
dc.relation.haspart [Publication 2]: Nicolau Gonçalves, Janne Nikkilä and Ricardo Vigário. Partial Clustering for Tissue Segmentation in MRI. In Lecture Notes in Advances in Neuro-Information Processing, M. Köppen and N. Kasabov and G. Coghill (Eds.): Proceedings of the 15th International Conference on Neuro-Information Processing, ICONIP 2008, pages 559-566, November 2008. DOI: 10.1007/978-3-642-03040-6_68
dc.relation.haspart [Publication 3]: Nicolau Gonçalves, Janne Nikkilä and Ricardo Vigário. Self–supervised MRI Tissue Segmentation by Discriminative Clustering. International Journal of Neural Systems, Volume 24, Number 1, 16 pages, January 2014, DOI 10.1142/S012906571450004X
dc.relation.haspart [Publication 4]: Hanna Jokinen, Nicolau Gonçalves, Ricardo Vigário, Jari Lipsanen, Franz Fazekas, Reinhold Schmidt, Frederik Barkhof, Philip Scheltens, Sofia Madureira, José M. Ferro, Domenico Inzitari, Leonardo Pantoni, Timo Erkinjuntti and the LADIS Study Group. A novel multispectral MRI tissue segmentation approach reveals early stage white matter lesions and predicts cognitive decline. Submitted to journal publication, October 2014.
dc.relation.haspart [Publication 5]: Nicolau Gonçalves, Hanna Jokinen, Franz Fazekas, Reinhold Schmidt, Frederik Barkhof, Philip Scheltens, Sofia Madureira, Ana Verdelho, Domenico Inzitari, Leonardo Pantoni, Timo Erkinjuntti and Ricardo Vigário. Prediction of cerebral white matter lesion evolution through self-supervised tissue segmentation. Submitted to journal publication, October 2014.
dc.relation.haspart [Publication 6]: Jayaprakash Rajasekharan, Ulrike Scharfenberger, Nicolau Gonçalves and Ricardo Vigário. Image Approach towards Document Mining in Neuroscientific Publications. In Lecture Notes in Advances in Intelligent Data Analysis IX, Paul R. Cohen and Niall M. Adams and Michael R. Berthold (Eds.): Proceedings of the 9th International Symposium, Intelligent Data Analysis 2010, pages 147-158, May 2010, DOI 10.1007/978-3-642-13062-5_15
dc.relation.haspart [Publication 7]: Nicolau Gonçalves, Gabriela Vranou and Ricardo Vigário. Towards automated image mining from reported medical images. In Computational Vision and Medical Image Processing IV, Proceedings of VipIMAGE 2013 - 4th ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, pages 255-261, October 2013, DOI 10.1201/b15810-46
dc.relation.haspart [Publication 8]: Nicolau Gonçalves, Erkki Oja and Ricardo Vigário. Medical document mining combining image exploration and text characterization. In Lecture Notes in Artificial Intelligence, Sašo Džeroski, Panče Panov, Dragi Kocev and Ljupčo Todorovski (Eds.): Proceedings of the 17th International Conference on Discovery Science, Discovery Science 2014, pages 99-110, October 2014, DOI 10.1007/978-3-319-11812-3_9
dc.subject.other Computer science en
dc.subject.other Medical sciences en
dc.title Advances in Analysis and Exploration in Medical Imaging en
dc.type G5 Artikkeliväitöskirja fi Perustieteiden korkeakoulu fi School of Science en
dc.contributor.department Tietojenkäsittelytieteen laitos fi
dc.contributor.department Department of Information and Computer Science en
dc.subject.keyword magnetic resonance imaging (MRI) en
dc.subject.keyword functional MRI en
dc.subject.keyword clustering en
dc.subject.keyword image segmentation en
dc.subject.keyword brain en
dc.subject.keyword self-supervised en
dc.subject.keyword machine learning en
dc.subject.keyword document mining en
dc.subject.keyword consistency estimation en
dc.subject.keyword neural diseases en
dc.identifier.urn URN:ISBN:978-952-60-5948-8
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Oja, Erkki, Aalto Distinguished Prof., Aalto University, Department of Information and Computer Science, Finland
dc.opn Madsen, Kristoffer H., Dr., Danish Research Centre for Magnetic Resonance, Denmark 2014-10-24
dc.contributor.lab Neuroinformatics Research Group en
dc.contributor.lab Neuroinformatiikka fi
dc.rev Nieminen, Miika, Prof., University of Oulu, Finland
dc.rev Kasabov, Nikola, Prof., Auckland University of Technology, New Zealand 2014-12-05

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