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ABSTRACT: Images produced by ultrasound systems are adversely hampered by a stochastic process known as speckle. A despeckling method based upon removing outlier is proposed. The method is developed to contrast enhance B-mode ultrasound images. The contrast enhancement is with respect to decreasing pixel variations in homogeneous regions while maintaining or improving differences in mean values of distinct regions. A comparison of the proposed despeckling filter is compared with the other well known despeckling filters. The evaluations of despeckling performance are based upon improvements to contrast enhancement, structural similarity, and segmentation results on a Field II simulated image and actual B-mode cardiac ultrasound images captured in vivo .
IEEE Transactions on Image Processing 08/2010; · 3.04 Impact Factor
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P.C. Tay
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ABSTRACT: This paper explores the incorporation of the two dimensional (2D) empirical mode decomposition, which is used in the Hilbert-Huang Transform, into a meaningful AM-FM image model framework. A virtue of the empirical mode decomposition is that it decomposes a non-stationary signal into stationary intrinsic mode functions and a residue signal. The empirical mode decomposition attempts to produce intrinsic mode functions that are stationary and a residue function that is dominated by piecewise monotonic functions. When considering image pixel values as produced from a non-stationary process, the 2D empirical mode decomposition shows promise as a precursor step in determining AM-FM components where stationarity is needed.
Image Analysis and Interpretation, 2008. SSIAI 2008. IEEE Southwest Symposium on; 04/2008
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ABSTRACT: A novel stochastically driven filtering method to despeckle B mode ultrasound images is presented. This method is motivated by viewing the pixel values as a stochastic process and removing outliers, where outliers are defined by local extrema. These outliers are removed by local averaging. This produces another image with new outliers (local extrema) and the process is iterated. With each iteration homogeneous regions become smoother while edges that defined these regions are preserved. By allowing a dynamically varying window to determine the local mean, we achieve equivalent results with fewer iterations
Image Processing, 2006 IEEE International Conference on; 11/2006
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ABSTRACT: A novel stochastically driven filtering method to despeckle B mode ultrasound images is presented. This method is motivated by viewing the pixel values as a stochastic process and removing outliers, where outliers are defined by local extrema. These outliers are removed by local averaging. This produces another image with new outliers (local extrema) and the process is iteratively repeated. With each iteration homogeneous regions become smoother while edges that defined these regions remain preserved. To evaluate the performance of our proposed method in satisfying these two opposing goals we develop a modified Fisher discriminant contrast metric. Larger values of this metric indicate better performance in reducing each intraregion or intraclass variance and increasing the difference of interregion or interclass means
Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on; 05/2006
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ABSTRACT: JPEG 2000 is a lossy integer-to-integer transform-based compression method that first quantizes the separable 2-D wavelet transform coefficients, then entropy codes them. The image is restored by performing an inverse wavelet transform on the dequantized and decoded coefficients. In this paper we consider closely related coding strategies using modulated lapped transforms rather than wavelet transforms, where performance is studied as a function of time (space) localization, frequency localization, and joint localization. In terms of reconstruction error at a given coding gain (quantization step size), we find that the modulated lapped transform admitting the best frequency localization offers superior performance relative to both other lapped transforms and orthogonal wavelet transforms.
Image Processing, 2005. ICIP 2005. IEEE International Conference on; 10/2005
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ABSTRACT: The paper implements the discrete wavelet transform in the discrete Fourier domain. The need for such an approach arose out of our desire to find a convenient means of realizing a new class of non-separable orientation selective 2D wavelet filter banks that are designed directly in the DFT domain. The filter bank design process begins with a conventional separable 2D perfect reconstruction parallel filter bank that is not orientation selective. In the DFT domain, each non-low pass channel is decomposed into the sum of two orientation selective frequency responses that are each supported on only two quadrants of the 2D frequency plane. The resulting filter bank possesses the good joint localization properties of orthogonal wavelet transforms and offers both perfect reconstruction and orientation selectivity. However, the orientation selective channels are non-separable - they cannot be implemented as iterated 1D convolutions according to the usual separable 2D wavelet transform paradigm. To overcome this difficulty, we develop straightforward techniques for implementing the DWT directly in the DFT domain.
Image Analysis and Interpretation, 2004. 6th IEEE Southwest Symposium on; 04/2004
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ABSTRACT: In this paper we study the joint time-frequency localization of cascade connections of maximally decimated wavelet filter banks. An M-channel bank is created by cascading a series of p<sub>i</sub>-channel maximally decimated filter banks where M=p1 p2.....pk is the prime factorization of M. Joint localization of the overall M-channel filter bank is quantified in terms of the geometric mean of certain discrete domain uncertainty measures of the analysis filters. As examples, we obtain interesting quantitative time-frequency localization measures for several two, four, and eight channel filter-banks.
Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on; 10/2003
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ABSTRACT: In this paper we present an unsupervised modulation domain technique for segmenting textured images. A dominant component AM-FM analysis is performed on the image, and estimates of the locally dominant amplitude and frequency modulations are extracted at each pixel. Modulation domain density clustering is then applied to estimate the maximum number of textured regions that might be present in the image. The feature space is augmented with horizontal and vertical spatial information prior to the application of k-means clustering to arrive at an initial image segmentation Connected components labeling with minor region removal and morphological smoothing are then applied to yield the final segmentation. We demonstrate the technique on several synthetic and natural images.
01/2002;
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ABSTRACT: We address two problems associated with the traditional approach to teaching a junior level course in signals and systems. First, students are expected to develop a facility with a variety of transforms and we have observed that this overwhelms many, reducing them to simply memorizing equations. Second, the usual treatment of Dirac's delta as a function not only leads to serious contradictions with the standard calculus, but also leaves intractable schisms surrounding the transforms of important harmonic functions. We present a unified and consistent approach designed to ameliorate these problems using abstract linear algebra and distribution theory. The approach was implemented for four semesters and exit surveys were conducted to assess pedagogical effectiveness
Frontiers in Education Conference, 2000. FIE 2000. 30th Annual; 02/2000
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ABSTRACT: We present an unsupervised modulation domain technique for segmenting textured images. A dominant component AM-FM analysis is performed on the image, and estimates of the locally dominant amplitude and frequency modulations are extracted at each pixel. Modulation domain density clustering is then applied to estimate the maximum number of textured regions that might be present in the image. The feature space is augmented with horizontal and vertical spatial information prior to the application of k-means clustering to arrive at an initial image segmentation. Connected components labeling with minor region removal and morphological smoothing are then applied to yield the final segmentation. We demonstrate the technique on several synthetic and natural images.
Signals, Systems and Computers, 2000. Conference Record of the Thirty-Fourth Asilomar Conference on; 02/2000
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ABSTRACT: Reverberation and multi-path reflection artifacts are a common problem in ultrasound imaging. We propose a novel method to remove these artifacts. Regions adversely affected by these artifacts are replaced with textures that resemble the underlying object(s), which were originally obscured. Our proposed method incorporates optimally soft thresholding the 2D discrete wavelet transform of the artifact regions to produce a near optimal estimate of the reflectivity values due only to the reverberation and multi-path reflection artifacts. Simply subtracting this estimate from the original reflectivity values, we attain a near optimal estimate of the artifact free reflectivity values. We provide simulated and B mode images to substantiate the benefits of this method in producing an artifact removed image
Image Analysis and Interpretation, 2006 IEEE Southwest Symposium on; 02/2001
