Non-local means kernel regression based despeckling of B-mode ultrasound images

Bharath, R and Rajalakshmi, P (2016) Non-local means kernel regression based despeckling of B-mode ultrasound images. In: IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), 14-16 September 2016, Munich, Germany.

Full text not available from this repository. (Request a copy)


Medical ultrasound scanning is a widely used diagnostic imaging modality in health-care. Speckle is inherent noise present in ultrasound images reducing the diagnostic accuracy of ultrasound scanning. Speckle noise contributes to high variance between pixels and delineates boundaries of the organs. Effective despeckling involves reducing the variance between pixels corresponding to homogeneous region and to preserve anatomical details simultaneously. Non-Local Means filters are highly successful and produced state of the art results in despeckling ultrasound images. In this paper, we show the effectiveness of Non-Local Means filter with polynomial regression kernel in despeckling ultrasound images. The proposed algorithm is evaluated on software simulated and real time ultrasound images and proved very effective in both despeckling and edge preservation.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: kernel regression, multiplicative noise, nonlocal Means, speckle filtering, ultrasound scanning
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 28 May 2019 06:25
Last Modified: 28 May 2019 06:25
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 5342 Statistics for this ePrint Item