Biomedical subjects

R C Chauhan

Publications and source records attributed to R C Chauhan.

7 recordsLinked to original sources

PubMed2005

Space-frequency quantiser design for ultrasound image compression based on minimum description length criterion.

The paper addresses the problem of how the spatial quantisation mode and subband adaptive uniform scalar quantiser can be jointly optimised in the minimum description length (MDL) framework for compression of ultrasound images. It has been shown that the statistics of wavelet coefficients in the medical ultrasound (US) image can be better approximated by the generalised Student t-distribution. By combining these statistics with the operational rate-distortion (RD) criterion, a space-frequency quantiser (SFQ) called the MDL-SFQ was designed, which used an efficient zero-tree quantisation technique for zeroing out the tree-structured sets of wavelet coefficients and an adaptive scalar quantiser to quantise the non-zero coefficients. The algorithm used the statistical 'variance of quantisation error' to achieve the different bit-rates ranging from near-lossless to lossy compression. Experimental results showed that the proposed coder outperformed the set partitioning in hierarchical trees (SPIHT) image coder both quantitatively and qualitatively. It yielded an improved compression performance of 1.01 dB over the best zero-tree based coder SPIHIT at 0.25 bits per pixel when averaged over five ultrasound images.

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PubMed2005

Robust non-homomorphic approach for speckle reduction in medical ultrasound images.

Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform (homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks, an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42dB over the best state-of-the-art despeckling method reported in the literature, 1.73dB over the Lee filter and 1.31dB over the Kaun filter at an input SNR of 12.0dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method suppressed the speckle noise well, while preserving the texture and organ surfaces.

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PubMed2004

Wavelet-based statistical approach for speckle reduction in medical ultrasound images.

A novel speckle-reduction method is introduced, based on soft thresholding of the wavelet coefficients of a logarithmically transformed medical ultrasound image. The method is based on the generalised Gaussian distributed (GGD) modelling of sub-band coefficients. The method used was a variant of the recently published BayesShrink method by Chang and Vetterli, derived in the Bayesian framework for denoising natural images. It was scale adaptive, because the parameters required for estimating the threshold depend on scale and sub-band data. The threshold was computed by Ksigma2/sigma(x), where sigma and sigma(x) were the standard deviation of the noise and the sub-band data of the noise-free image, respectively, and K was a scale parameter. Experimental results showed that the proposed method outperformed the median filter and the homomorphic Wiener filter by 29% in terms of the coefficient of correlation and 4% in terms of the edge preservation parameter. The numerical values of these quantitative parameters indicated the good feature preservation performance of the algorithm, as desired for better diagnosis in medical image processing.

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PubMedDate not supplied

Homomorphic wavelet thresholding technique for denoising medical ultrasound images.

A novel homomorphic wavelet thresholding technique for reducing speckle noise in medical ultrasound images is presented. First, we show that the speckle wavelet coefficients in the logarithmically transformed ultrasound images are best described by the Nakagami family of distributions. By exploiting this speckle model and the Laplacian signal prior, a closed form, data-driven, and spatially adaptive threshold is derived in the Bayesian framework. The spatial adaptivity allows the additional information of the image (such as identification of homogeneous or heterogeneous regions) to be incorporated into the algorithm. Further, the threshold has been extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. Experimental results demonstrate the improved performance of the proposed method over other well-known speckle reduction filters. The application of the proposed method to a realistic US test image shows that the new technique, named HomoGenThresh, outperforms the best wavelet-based denoising method reported in [1] by more than 1.6 dB, Lee filter by 3.6 dB, Kaun filter by 3.1 dB and band-adaptive soft thresholding [2] by 2.1 dB at an input signal-to-noise ratio (SNR) of 13.6 dB.

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PubMedDate not supplied

Joint thresholding and quantizer selection for compression of medical ultrasound images in the wavelet domain.

This paper introduces a simple and efficient technique for compression of medical ultrasound (US) images in the wavelet domain. The statistics of subband wavelet coefficients are modelled using the generalized Gaussian distribution (GGD). By exploiting these statistics, a uniform scalar quantizer is designed which adapts very well to the changing statistics of the signal across various subbands and scales. To increase the quantization performance, a threshold is chosen adaptively to zero-out the insignificant wavelet coefficients in the detail subbands before quantization. A distinctive feature of the proposed technique is that it unifies the two approaches to image adaptive coding: rate-distortion (R-D) optimized quantizer selection and R-D optimal thresholding, in order to increase the compression performance of the coder. The operational R-D criterion used for joint optimization is derived in the minimum description length (MDL) framework. The experimental results show that the joint R-D optimization leads to significant improvement in the compression performance of the proposed coder, named JTQ-WV, over the best state-of-the-art image coder, SPIHT. For example, the coding of US images at 0.25 bpp by JTQ-WV yields a PSNR gain of 1.0 dB over the benchmark SPIHT.

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PubMedDate not supplied

Wavelet based compression of medical ultrasound images using vector quantization.

In this paper, an efficient technique for compression of medical ultrasound (US) images is proposed. The technique is based on wavelet transform of the original image combined with vector quantization (VQ) of high-energy subbands using the LBG algorithm. First, we analyse the statistical behaviour of wavelet coefficients in US images across various subbands and scales. The analysis show that most of the image energy is concentrated in one of the detail subband, either in the vertical detail subband (most of the time) or in the horizontal subband. The other two subbands at each decomposition level contribute negligibly to the total image energy. Then, by exploiting this statistical analysis, a low-complexity image coder is designed, which applies VQ only to the highest energy subband while discarding the other detail subbands at each level of decomposition. The coder is tested on a series of abdominal and uterus greyscale US images. The experimental results indicate that the proposed method clearly outperforms the JPEG2000 (Joint Photographers Expert Group) encoder both qualitatively and quantitatively. For example, without using any entropy coder, the proposed method yields a peak signal to noise ratio gain of 0.2 dB to 1.2 dB over JPEG2000 on medical US images.

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PubMedDate not supplied

Performance improvement of the SPIHT coder based on statistics of medical ultrasound images in the wavelet domain.

This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.

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