Glial Tumors: Quantification and Segmentation from MRI and MRS

Brain tumor segmentation is an important technique in computer-assisted diagnosis. To improve brain tumor diagnosis, it is necessary to use biochemical information provided by magnetic resonance spectroscopy (MRS). Some of the challenges involved are determining how to combine multimodal signals such as proton MRS and morphological or structural images, then knowing how to use the combined information to make a decision. In this paper, we propose using a data fusion method to perform an automatic segmentation of the brain tumor areas. To diagnose brain tissue abnormalities such as tumors, we need to use multispectral magnetic resonance images (MRIs), e.g. T1-weighted, T2-weighted, gadolinium, or FLAIR to determine some properties of the tumor, including size, position, sort, and relationship with other tissues. However, the tumor type and grade are usually diagnosed from histopathological examination of a surgical specimen. Proton (1H) magnetic resonance spectroscopy (MRS) is a non invasive MR technique that provides biochemical information about metabolites. The major biochemical characteristics can provide useful information about brain tumor type and grade (Howe et al., 2003a; 2003b). In many studies, the use of in vivo 1H-MRS has been described for determining tumor type and grade (Howe et al., 2003a; Howe et al., 2003b; Preul et al., 1996; Majós et al., 2009). Since analysis of in vivo MRS measurements are dependent on acquisition techniques that could compromise the spatial resolution and accuracy of resulting metabolite values (Maudsley et al., 2009), metabolic changes associated with disease are frequently small and diffuse. Furthermore, using the chemical-shift imaging (CSI) technique, metabolite images, known as MR spectroscopic imaging (MRSI) images, can be created using multivoxel MRS information, but it is not as visually interpretable as in a structural MRI (Maudsley et al., 2009). So, for tumor tissue classification, it is important that MRSI be combined with MRI to make a more accurate estimation of variations in metabolites, support hypotheses of metabolite variation causes, and to yield sufficient information about the tissue. For over a decade, scientists have been developing automated brain-tumor classification methods using MRS (Garcia-Gomez et al., 2009), but a more clear definition of brain tumor type and grade may be obtained by combining MRS or MRSI and MRI (Garcia-Gomez et al., 2009). A technique used to differentiate glioblastoma from metastatic lesions using MRI and