Clinical-routine MRSI-data analysis is commonly performed through the visual inspection of multiple metabolite and metabolite-ratio maps, and aims at translating the different spectroscopic patterns into known tissue-types, such as, necrosis, solid tumour, tumour-infiltration, normal-brain-tissue, etc. Such translation/segmentation requires solid expertise in MR-spectroscopy, which most clinicians do not have. Bad-quality-data, as well as frequency-dependant-selection-profiles further complicate proper interpretation of MRSI-data. Therefore, to facilitate the clinical-use of MRSI, we present an automatic MRSI-tissue-type segmentation algorithm, that includes automatic-quality-filtering and selection-profile-correction. The method was tested in glioblastoma and the tissue-types were compared against an MRI-based tumour-segmentation-method.