Bone attenuation remains a methodological challenge in hybrid PET/MR, as bone is hard to visualize via magnetic resonance imaging (MRI). Therefore, novel methods for taking into account bone attenuation in MR-based attenuation correction (MRAC) are needed. In this study, we propose a tissue-probability based attenuation correction (TPB-AC), which employs the commonly available neurological toolbox SPM8, to derive a subject-specific mu-map by segmentation of T1-weighted MR images. The procedures to derive a mu-map representing soft tissue, air and bone from the New Segment function in SPM8 and MATLAB are described. Visual and quantitative comparisons against CT-based attenuation correction (CTAC) data were performed using two mu-values (0.135 cm(-1) and 0.145 cm(-1)) for bone. Results show improvement of visual quality and quantitative accuracy of positron emission tomography (PET) images when TPB-AC mu-map is used in PET/MR image reconstruction. Underestimation in PET images was decreased by an average of 5+/-2 percent in the whole brain across all patients. In addition, the method performed well when compared to CTAC, with maximum differences (mean +/- standard deviation) of -3 +/- 2 percent and 2 +/- 4 percent in two regions out of 28. Finally, the method is simple and computationally efficient, offering a promising platform for further development. Therefore, a subject-specific MR-based mu-map can be derived only from the tissue probability maps from the New Segment function of SPM8.