Edge preserving wavelet speckle filtering

Medeiros, Fátima N. S. de; Mascarenhas, Nelson D. A.; Marques, Régis C. P.; Laprano, C.M.

doi:10.1109/iai.2002.999933

Cited by 23 publications

(9 citation statements)

References 10 publications

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“…[24] Coherence enhancing diffusion. nldif Wavelet [15], [25]- [29], [35] Only the useful wavelet coefficients are used. waveltc following categories: local statistics, median filtering, homogeneity, geometric, homomorphic, anisotropic diffusion, and wavelet filtering.…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

Loizou

Pattichis

Christodoulou

et al. 2005

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

292

239

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It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.

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Section: Introductionmentioning

confidence: 99%

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

Loizou

Pattichis

Christodoulou

et al. 2005

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

292

239

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“…Furthermore, wavelet filtering proposed by Donoho in [31] was investigated for suppressing the speckle noise in SAR images [16,37], real-world images [27], and ultrasound images [28] with favorable results. In this study, it is shown that the DsFwaveltc filter gave poorer performance for removing the speckle noise from the ultrasound images of the carotid artery (Tables 7.2…”

Section: Discussionmentioning

confidence: 99%

Atherosclerosis Disease Management

Suri¹,

Kathuria²,

Molinari³

2011

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It is well known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. This necessitates the need for robust despeckling techniques for both routine clinical practice and tele-consultation. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and tele-consultation. The objective of this chapter is to introduce the theoretical background of a number of despeckle filtering techniques and to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts, on ultrasound images of the carotid artery bifurcation. In this chapter, a total of ten despeckle filters are presented based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. Our results suggest that the first-order statistics filter DsFlsmv gave the best performance, followed by the geometric filter DsFgf4d and the homogeneous mask area filter DsFlsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. Most importantly, a despeckle filtering and evaluation protocol is proposed based on texture analysis, image quality evaluation metrics, and visual evaluation by experts. In conclusion, the proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.

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“…In the same year Khaled et al proposed the use of nonlinear anisotropic diffusion for real time speckle reduction [8]. Later in the same year Medeiros et al proposed speckle noise filtering using edge preserving wavelet [9]. Few years later Gupta et al proposed wavelet based statistical approach for speckle reduction from medical ultrasound image [10].…”

Section: Introductionmentioning

confidence: 99%

Wavelet based non local means filter for despeckling of intravascular ultrasound image

China

Mitra

Chakraborty

et al. 2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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In this paper, a novel methodology has been proposed for speckle noise reduction of intravascular ultrasound (IVUS). IVUS, a standard coronary artery diagnosis imaging protocol, is degraded with speckle noise due to the coherent interferences of the ultrasound reflected from the scatters. Presence of such noise creates problem in segmentation and classification of these images. Non local means filter has been applied in wavelet domain to smooth out noise interferences to improve the visual characteristics of IVUS insight. Eventually, a comparison study has also been performed with various filters viz. anisotropic diffusion filter, nonlinear median filter and geometric nonlinear diffusion filter. Mean squared error (MSE) and peak signal to noise ratio (PSNR) has been used to evaluate the efficiency of the proposed filtering methodology.

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Edge preserving wavelet speckle filtering

Cited by 23 publications

References 10 publications

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

Atherosclerosis Disease Management

Wavelet based non local means filter for despeckling of intravascular ultrasound image

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