An Approach for Reducing Outliers of Non Local Means Image Denoising Filter
This is an incremental improvement for medical imaging, specifically enhancing ultrasound image denoising to guide injection therapy for lumbar radiculopathy.
The authors tackled the problem of outliers in non-local means image denoising by proposing an adaptive method called NLACM, which uses median within a statistically defined range to improve denoising quality, showing better performance at high noise levels compared to traditional NLM.
We propose an adaptive approach for non local means (NLM) image filtering termed as non local adaptive clipped means (NLACM), which reduces the effect of outliers and improves the denoising quality as compared to traditional NLM. Common method to neglect outliers from a data population is computation of mean in a range defined by mean and standard deviation. In NLACM we perform the median within the defined range based on statistical estimation of the neighbourhood region of a pixel to be denoised. As parameters of the range are independent of any additional input and is based on local intensity values, hence the approach is adaptive. Experimental results for NLACM show better estimation of true intensity from noisy neighbourhood observation as compared to NLM at high noise levels. We have verified the technique for speckle noise reduction and we have tested it on ultrasound (US) image of lumbar spine. These ultrasound images act as guidance for injection therapy for treatment of lumbar radiculopathy. We believe that the proposed approach for image denoising is first of its kind and its efficiency can be well justified as it shows better performance in image restoration.