In this study the evaluation of a Platelet-based Maximum Penalized Likelihood Estimation (MPLE) for denoising SPECT images was performed and compared with other denoising methods such as Wavelets or Butterworth filteration. Platelet-based MPLE factorization as a multiscale decomposition approach has been already proposed for better edges and surfaces representation due to Poisson noise and inherent smoothness of this kind of images. We applied this approach on both simulated and real SPECT images. For NEMA phantom images, the measured noise levels before (M(b)) and after (M(a)) denoising with Platelet-based MPLE approach were M(b)=2.1732, M(a)=0.1399. In patient study for 32 cardiac SPECT images, the difference between noise level and SNR before and after the approach were (M(b)=3.7607, SNR(b)=9.7762, M(a)=0.7374, SNR(a)=41.0848) respectively. Thus the Coefficient Variance (C.V) of SNR values for denoised images with this algorithm as compared with Butterworth filter, (145/33%) was found. For 32 brain SPECT images the Coefficient Variance of SNR values, (196/17%) was obtained. Our results shows that Platelet-based MPLE is a useful method for denoising SPECT images considering better homogenous image, improvements in SNR, better radioactive uptake in target organ and reduction of interfering activity from background radiation to compare to that of other conventional denoising methods.