Image denoising has become a very essential exercise all through the diagnosis especially in case of medical image processing involving ultrasound. Speckle is a multiplicative noise that degrades ultrasound images. The existence of speckle noise in ultrasound images reduces its resolution and contrast there by degrading the diagnostic accuracy of the ultrasound image. The presence of speckle noise in fetal ultrasound images make the conditions worse to carry out prenatal diagnosis of congenital heart disease. This is due to the impact of edge and local fine details that are not very clear for diagnosis. Thus there is a vital need for the development of a robust speckle reduction filter to enhance the quality of the speckle affected image and to preserve the essential features. In this paper, we propose a despeckling filter which is based on the concept of binary connectedness that uses an algorithm for computing the degree of connectedness of a pixel to all the other in a subjective neighborhood and it distinguishes the edge and background region present in an image. The proposed filter utilizes the Rayleigh distribution to model the speckle noise and establishes binary connectedness to distinguish edge from background region hence called as Binary connectedness based RML filter. The performance of the proposed filter is tested and compared with several existing despeckling filters including Median, Kuwahura and Frost filters to prove its expertise in terms several performance indices and image profile. Experimental results shows that the proposed filter removes the speckle noise effectively and thus outshine the conventional filters.

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Binary Connectedness Rayleigh Distribution Maximum Likelihood Estimator Despeckling Edge And Background Detection.