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• W87725907 abstract "The medical diagnosis of brain tumours is one of the main applications of Magnetic Resonance (MR). Magnetic Resonance consists of two main branches: Imaging and Spectroscopy. Magnetic Resonance Imaging is the radiologic technique applied to produce high-quality images for diagnostic purposes. Magnetic Resonance Spectroscopy provides chemical information about metabolites present in the brain, such as their concentrations. Both Imaging and Spectroscopy can be exploited for the grading and typing of brain tumours, also called classification. The present gold standard to diagnose an abnormal brain mass is the histopathological analysis of a biopsy. However, a biopsy is riskful for the patient and therefore it would be very benificial if a diagnostic tool based on non-invasive techniques such as MR would be used to aid or even avoid the current gold standard. Classification of brain tumours is very interdisciplinairy and involves many aspects of medicine, engineering and mathematics. The development of a medical decision support tool covers data collection, specific pre-processing, exploitation of the useful features for classification and testing of the classification. The domain-specific knowledge of neurologists and radiologists is invaluable to guarantee a diagnostic tool applicable in daily clinical practice. This chapter provides an overview of the NMR methodology and its applications to brain tumour diagnosis. Spectral pre-processing issues such as normalization and baseline correction, which could have an influence on the accuracy of the classification, are discussed. A wealth of methods exists for feature extraction and classification of MR data; principal component analysis and mixture modelling are covered as unsupervised techniques and linear discriminant analysis and support vector machines as supervised ones. The described methods were tested on MRI and MRS data of healthy as well as brain tumour tissue, acquired in the framework of the EU-funded INTERPRET project. Results of the INTERPRET project illustrate that imaging and spectroscopic data are complementary for the accurate diagnosis of brain tumour tissue. The chapter arguments for a strong focus on the fusion of MR imaging and spectroscopic data and non-MR data, in the framework of further development and improvement of a medical decision support tool." @default.
• W87725907 created "2016-06-24" @default.
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• W87725907 date "2007-01-01" @default.
• W87725907 modified "2023-09-27" @default.
• W87725907 title "Classification of Brain Tumours by Pattern Recognition of Magnetic Resonance Imaging and Spectroscopic Data" @default.
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• W87725907 doi "https://doi.org/10.1016/b978-044452855-1/50013-1" @default.
• W87725907 hasPublicationYear "2007" @default.
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