Medical images are often deteriorated by noise due to various sources of interferences and other phenomena that affect the measurement processes. The varieties include Speckle noise, Gaussian noise, Salt and pepper noise. It is a difficult task to separate noise from an image while maintaining the desired information and quality of an image. In the field of biomedical imaging, the ultrasound (US) B-Scan images are used for tissue characterization. These images are obtained with a simple linear or sector scan US probe, which show a granular appearance called speckle. Speckle is modeled as a signal dependent noise, which tends to reduce the image resolution and contrast, thereby reducing the diagnostic values of the US imaging modality. Over a period, various speckle reduction techniques have been developed by researchers did not represent a comprehensive method that takes all the constraints into consideration. The results obtained are presented in the form of filtered images, statistical tables and diagrams. Based on the statistical measures and visual quality of the US B-scan images the Wiener filter performed well over the other filter techniques. The paper represents comparison of spatial filters approaches i.e. filtering approach using linear and Non-linear filters accounting Peak Signal to Noise Ratio, Root Mean Square Error, and Universal Quality Index, Structural Similarity Index and Run Time as performance parameters. This paper proves that Weiner filtering method is very effective for all types of noise.

Speckle noise Ultrasound image spatial filtering speckle suppression.