Enhancement of Despeckled Ultrasound Images by Forward-Backward Diffusion

Nieniewski, Mariusz

doi:10.1007/978-3-319-11331-9_54

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…would take too much place and here only the names of the filters and their popular abbreviations are specified (compare also The Non-Local Means Filter, denoted DsNLM here, was obtained from Buades et al (2005) and Manjon-Herrera (2016). The Nonlinear Coherent Diffusion Filter, denoted DsNCD, was taken from Nieniewski and Zajączkowski (2014), and Forward-Backward Diffusion Filter DsFBD from Nieniewski and Zajączkowski (2016). In order to make possible comparisons all filters have to process a common image, and it was assumed that this image is logcompressed since this was the most common case from the point of view of the software availability.…”

Section: Consideration Of the Details Of Loizou Filtersmentioning

confidence: 99%

“…In the case of the DsNLM filter the parameters were: search window of side t = 5, similarity window of side f = 2, and filtering parameter h = 10 ( Buades et al, 2005;Manjon-Herrera, 2016). For the DsNCD filter the parameters were taken from Nieniewski (2014). Inspection of the column designated Fig.…”

Section: Experiments With Computer-generated Imagementioning

confidence: 99%

“…In another experiment the same image of Fig. 1(b) was filtered by means of the DsNCD filter (Nieniewski and Zajączkowski, 2014). In this case the conditions were more uniform in the sense that only one filter was used.…”

Section: Experiments With Computer-generated Imagementioning

confidence: 99%

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Comparison of Ultrasound Image Filtering Methods by Means of Multivariable Kurtosis

Nieniewski¹,

Zajączkowski²

2017

Image Anal Stereol

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Comparison of the quality of despeckled US medical images is complicated because there is no image of a human body that would be free of speckles and could serve as a reference. A number of various image metrics are currently used for comparison of filtering methods; however, they do not satisfactorily represent the visual quality of images and medical expert’s satisfaction with images. This paper proposes an innovative use of relative multivariate kurtosis for the evaluation of the most important edges in an image. Multivariate kurtosis allows one to introduce an order among the filtered images and can be used as one of the metrics for image quality evaluation. At present there is no method which would jointly consider individual metrics. Furthermore, these metrics are typically defined by comparing the noisy original and filtered images, which is incorrect since the noisy original cannot serve as a golden standard. In contrast to this, the proposed kurtosis is the absolute measure, which is calculated independently of any reference image and it agrees with the medical expert’s satisfaction to a large extent. The paper presents a numerical procedure for calculating kurtosis and describes results of such calculations for a computer-generated noisy image, images of a general purpose phantom and a cyst phantom, as well as real-life images of thyroid and carotid artery obtained with SonixTouch ultrasound machine. 16 different methods of image despeckling are compared via kurtosis. The paper shows that visually more satisfactory despeckling results are associated with higher kurtosis, and to a certain degree kurtosis can be used as a single metric for evaluation of image quality.

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Section: Consideration Of the Details Of Loizou Filtersmentioning

confidence: 99%

Section: Experiments With Computer-generated Imagementioning

confidence: 99%