Speckle Noise Reduction of Ultrasound Images Using BFO Cascaded with Wiener Filter and Discrete Wavelet Transform in Homomorphic Region

Abstract Speckle noise removal is a key issue in ultrasound image processing used for getting important diagnostic information for human body. Speckle noise degrades the visual evaluation of ultrasound images. The main challenge of despeckling is to preserve all the fine details and the edges of the ultrasonographic images. From various speckle removable methods, there is a type of method which converts the multiplicative behaviour of speckle noise in to additive by using log transform. The additive noise removal is easy as compared to multiplicative noise. Here a new approach for denoising of highly distorted images affected by speckle noise is proposed. The proposed method is realized using bacterial foraging optimization (BFO) cascaded with wavelet transform and wiener filter in a homomorphic framework. The wavelet packet decomposition is used to identify and remove the noise from affected pixels. Wiener filter is used for pre-processing purpose. The BFO algorithm is used to reduce the amount of error between the speckled image and the Despeckled output image from homomorphic framework after processing. It is used to maintain the finer details and the error percentage considered is 0.0001. The proposed technique provides superior results in comparison to other techniques in form of peak signal to noise ratio (PSNR), Mean Absolute Error(MAE), clarity andpreservation of finer details.