Photoacoustics is a hybrid modality used to image biological tissues. As optical absorption of tissues depends on the wavelength of the transmitted light, multispectral photoacoustic datasets can be obtained by changing this wavelength. This study presents a regularization method to segment multispectral photoacoustic images based on both the spatial and spectral features of the dataset pixels. The proposed processing is adapted from the spatiotemporal mean-shift approach and cluster patterns with similar spectral profiles, i.e., the variation of the received amplitude among the wavelengths, independent of their initial position. The segmentation performance of this method has been experimentally tested on multispectral photoacoustic tomographic data. We initially used a phantom that contained fresh and stale liver samples, and then a second phantom that contained two blood dilutions or a colored absorber. Experimentally, a clustering performance greater than 98% is achieved. This method makes it possible to discriminate between different media, between the same medium as fresh or stale, and between the same medium with different dilutions.