Background - Few indexes are available for nuclear medicine image quality assessment, particularly for respiratory blur assessment. A variety of methods for the identification of blur parameters has been proposed in literature mostly for photographic pictures but these methods suffer from a high sensitivity to noise, making them unsuitable to evaluate nuclear medicine images. In this paper, we aim to calibrate and test a new blur index to assess image quality. Material and Methods – Blur index calibration was evaluated by numerical simulation for various lesions size and intensity of uptake. Calibrated blur index was then tested on gamma- camera phantom acquisitions, PET phantom acquisitions and real-patient PET images and compared to human visual evaluation. Results – For an optimal filter parameter of 9, non-weighted and weighted blur index led to an automated classification close to the human one in phantom experiments and identified each time the sharpest image in all the 40 datasets of four images. Weighted blur index was significantly correlated to human classification (ρ= 0.69 [0.45 ;0.84], p<0.001) when used on patient PET acquisitions. Conclusion – The provided index allows to objectively characterize the respiratory blur in nuclear medicine acquisition, whether in planar or tomographic images and might be useful in respiratory gating applications.