The Experts below are selected from a list of 58485 Experts worldwide ranked by ideXlab platform
Xudong Jiang - One of the best experts on this subject based on the ideXlab platform.
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weighted iterative truncated Mean Filter
IEEE Transactions on Signal Processing, 2013Co-Authors: Zhenwei Miao, Xudong JiangAbstract:The iterative truncated arithmetic Mean (ITM) Filter was proposed recently. It offers a way to estimate the sample median by simple arithmetic computing instead of the time consuming data sorting. In this paper, a rich class of Filters named weighted ITM (WITM) Filters are proposed. By iteratively truncating the extreme samples, the output of the WITM Filter converges to the weighted median. Proper stopping criterion makes the WITM Filters own merits of both the weighted Mean and median Filters and hence outperforms the both in some applications. Three structures are designed to enable the WITM Filters being low-, band- and high-pass Filters. Properties of these Filters are presented and analyzed. Experimental evaluations are carried out on both synthesis and real data to verify some properties of the WITM Filters.
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Iterative Truncated Arithmetic Mean Filter and Its Properties
IEEE Transactions on Image Processing, 2012Co-Authors: Xudong JiangAbstract:The arithmetic Mean and the order statistical median are two fundamental operations in signal and image processing. They have their own merits and limitations in noise attenuation and image structure preservation. This paper proposes an iterative algorithm that truncates the extreme values of samples in the Filter window to a dynamic threshold. The resulting nonlinear Filter shows some merits of both the fundamental operations. Some dynamic truncation thresholds are proposed that guarantee the Filter output, starting from the Mean, to approach the median of the input samples. As a by-product, this paper unveils some statistics of a finite data set as the upper bounds of the deviation of the median from the Mean. Some stopping criteria are suggested to facilitate edge preservation and noise attenuation for both the long- and short-tailed distributions. Although the proposed iterative truncated Mean (ITM) algorithm is not aimed at the median, it offers a way to estimate the median by simple arithmetic computing. Some properties of the ITM Filters are analyzed and experimentally verified on synthetic data and real images.
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Further Properties and a Fast Realization of the Iterative Truncated Arithmetic Mean Filter
IEEE Transactions on Circuits and Systems II: Express Briefs, 2012Co-Authors: Zhenwei Miao, Xudong JiangAbstract:The iterative truncated arithmetic Mean (ITM) Filter has been recently proposed. It possesses merits of both the Mean and median Filters. In this brief, the Cramer-Rao lower bound is employed to further analyze the ITM Filter. It shows that this Filter outperforms the median Filter in attenuating not only the short-tailed Gaussian noise but also the long-tailed Laplacian noise. A fast realization of the ITM Filter is proposed. Its computational complexity is studied. Experimental results demonstrate that the proposed algorithm is faster than the standard median Filter.
Shira L Broschat - One of the best experts on this subject based on the ideXlab platform.
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aggressive region growing for speckle reduction in ultrasound images
Pattern Recognition Letters, 2003Co-Authors: Yan Chen, Patrick J Flynn, Shira L BroschatAbstract:Abstract Speckle appears in all conventional medical B-mode ultrasonic images and can be an undesirable property since it may mask small but diagnostically significant features. In this paper, an adaptive Filtering algorithm is proposed for speckle reduction. It selects a Filtering region size using an appropriately estimated homogeneity value for region growth. Homogeneous regions are processed with an arithmetic Mean Filter. Edge pixels are Filtered using a nonlinear median Filter. The performance of the proposed technique is compared to two other methods––the adaptive weighted median Filter and the homogeneous region growing Mean Filter. Results of processed images show that the method proposed reduces speckle noise and preserves edge details effectively.
Yan Chen - One of the best experts on this subject based on the ideXlab platform.
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aggressive region growing for speckle reduction in ultrasound images
Pattern Recognition Letters, 2003Co-Authors: Yan Chen, Patrick J Flynn, Shira L BroschatAbstract:Abstract Speckle appears in all conventional medical B-mode ultrasonic images and can be an undesirable property since it may mask small but diagnostically significant features. In this paper, an adaptive Filtering algorithm is proposed for speckle reduction. It selects a Filtering region size using an appropriately estimated homogeneity value for region growth. Homogeneous regions are processed with an arithmetic Mean Filter. Edge pixels are Filtered using a nonlinear median Filter. The performance of the proposed technique is compared to two other methods––the adaptive weighted median Filter and the homogeneous region growing Mean Filter. Results of processed images show that the method proposed reduces speckle noise and preserves edge details effectively.
Yongtian Wang - One of the best experts on this subject based on the ideXlab platform.
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local statistics and non local Mean Filter for speckle noise reduction in medical ultrasound image
Neurocomputing, 2016Co-Authors: Jian Yang, Jingfan Fan, Xuehu Wang, Yongchang Zheng, Songyuan Tang, Yongtian WangAbstract:Medical ultrasound images are corrupted by speckle noise, which is multiplicative. This noise limits the contrast resolution in these images and complicates image-based quantitative measurement and diagnosis. In this study, the speckle noise in the ultrasound image is modeled by local statistics of the intensity distribution. And the non-local Mean (NLM) Filter is utilized to Filter additional noise by applying the redundancy information in noisy images. A hybrid denoising method is proposed in consideration of the characteristics of both the local statistics of speckle noise and the NLM Filter. The study combines local statistics with the NLM Filter to reduce speckle in ultrasound images. The local statistics of speckle noise is estimated by local patches, while the intensity of the denoising pixel is computed by the weighted average of all the pixels by using the NLM. The weight is determined according to the similarity measures between the intensities of the local patches. The performance of the proposed method is evaluated on synthetic data, simulated images, and real images. Results of quantitative analysis and visual inspection of the synthetic data and of the real images demonstrate that the proposed method outperforms the original NLM, as well as many previously developed methods.
Patrick J Flynn - One of the best experts on this subject based on the ideXlab platform.
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aggressive region growing for speckle reduction in ultrasound images
Pattern Recognition Letters, 2003Co-Authors: Yan Chen, Patrick J Flynn, Shira L BroschatAbstract:Abstract Speckle appears in all conventional medical B-mode ultrasonic images and can be an undesirable property since it may mask small but diagnostically significant features. In this paper, an adaptive Filtering algorithm is proposed for speckle reduction. It selects a Filtering region size using an appropriately estimated homogeneity value for region growth. Homogeneous regions are processed with an arithmetic Mean Filter. Edge pixels are Filtered using a nonlinear median Filter. The performance of the proposed technique is compared to two other methods––the adaptive weighted median Filter and the homogeneous region growing Mean Filter. Results of processed images show that the method proposed reduces speckle noise and preserves edge details effectively.