2010
DOI: 10.5120/1683-2227
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Performance Analysis of Filters for Speckle Reduction in Medical Ultrasound Images

Abstract: Speckle is a random multiplicative noise which obscures the perception and extraction of fine details in ultrasound image and despeckling is necessary to improve the visual quality for better diagnoses. Preliminary treatment of images before segmentation and classification includes despeckling as one of the important steps. This paper aims at introducing the possible range of image speckle corrections available.

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Cited by 15 publications
(10 citation statements)
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“…The most significant features such as area, diameter, and perimeter have been estimated from the segmented fibroid images as shown in Table 1, and the graphical representation is shown in Figs. 5,6. Based on the assessment of these parameters, the physicians will decide the nature of the treatment.…”
Section: Resultsmentioning
confidence: 99%
“…The most significant features such as area, diameter, and perimeter have been estimated from the segmented fibroid images as shown in Table 1, and the graphical representation is shown in Figs. 5,6. Based on the assessment of these parameters, the physicians will decide the nature of the treatment.…”
Section: Resultsmentioning
confidence: 99%
“…Initially, speckle suppression was performed by filtering procedure already applied in synthetic aperture radar (SAR) imagery [13]. The main drawback of such filtering is that the whole processed image becomes smooth, because the same filtering criterion is applied to all image pixels, irrespective they belong to homogenous regions or to edges.…”
Section: Review Of Some New Methods For Analyzing Vital Heart Signalsmentioning
confidence: 99%
“…Speckles represent the multiplicative noise and are immanent in ultrasound images [13,14]. Due to speckles, …”
Section: A Signal Preprocessingmentioning
confidence: 99%
“…We can work on that part of image where information content present is more by providing large window size to that area and where information content is low we can provide small window size, so we can adjust the window size in wavelet transform which other transforms available such as Fourier transform and Hilbert transform does not provide. Another main advantage of using discrete wavelet transformation is that after transformation it will not only provide frequency and amplitude information of signal but also provides temporal information whereas in other transformations temporal information is lost [6]. Filtering techniques are used so as to improve peak signal to noise ratio and to reduce mean square error ant to enhance the edges and lines of the image.…”
Section: Introductionmentioning
confidence: 99%