New spatially adaptive wavelet-based method for the despeckling of medical ultrasound images

Medical ultrasound images are widely used for diagnostic purposes. A major problem regarding these images is in their inherent corruption by speckle noise in a multiplicative fashion. The presence of speckle noise severely hampers the interpretation and analysis of medical ultrasound images. This paper presents a fast and reliable wavelet-based method for reducing the speckle in medical ultrasound images. A wavelet-based Bayesian maximum a posteriori denoiser is developed in a homomorphic framework. The wavelet coefficients of the log-transformed signal are modelled by a conditional Gaussian distribution, whereas those of the log-transformed speckle with a Maxwell distribution. The signal variances are obtained by using the local neighbors thus, making the method spatially adaptive. Simulations are performed using synthetically speckled and real ultrasound images. The results show that the proposed method can perform better than some of the existing methods in terms of the signal-to-noise ratio. Furthermore, the proposed method is fast, and preserves diagnostically important details.