Speckle reduction in breast cancer ultrasound images by using homogeneity modified bayes shrink

• This paper presents an algorithm for speckle reduction in breast ultrasound images.
• Our algorithm is based on homogeneity modified bayes shrink technique.
• We use seven different metrics for image quality evaluating.
• Photographic, synthetic, fetus and real ultrasound images use for validating the obtained results.

Ultrasound imaging suffers from severe artifacts caused by speckle noise. The paper introduces an algorithm for speckle noise reduction in breast cancer ultrasound images. Based on wavelet analysis and filtering, we employed a combination of homogeneity filtering and modified bayes shrink methods to remove noise while keeping the sharpness of important features. First, we replace pixel intensity by the mean of homogenous neighborhood and then, the threshold value of modified bayes shrink is employed to distinguish homogenous regions from regions with speckle noise obtained from homogeneity filtering. The proposed algorithm is called Homogeneity Modified Bayes Shrink (HMBS). A comparative study with other despeckling methods, using quantitative indices, showed the superiority of the proposed method over those methods.

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