Noise removal is a crucial step to enhance the quality of ultrasound images. However, some existing despeckling methods cannot ensure satisfactory restoration performance. In this paper, an adaptive non-local means (ANLM) filter is proposed for speckle noise reduction in ultrasound images. The distinctive property of the proposed method lies in that the decay parameter will not take the fixed value for the whole image but adapt itself to the variation of the local features in the ultrasound images. In the proposed method, the pre-filtered image will be obtained using the traditional NLM method. Based on the pre-filtered result, the local gradient will be computed and it will be utilized to determine the decay parameter adaptively for each image pixel. The final restored image will be produced by the ANLM method using the obtained decay parameters. Simulations on the synthetic image show that the proposed method can deliver sufficient speckle reduction while preserving image details very well and it outperforms the state-of-the-art despeckling filters in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Experiments on the clinical ultrasound image further demonstrate the practicality and advantage of the proposed method over the compared filtering methods.
KEYWORDS: Optical coherence tomography, Speckle, Image filtering, Signal to noise ratio, Anisotropic filtering, Nonlinear filtering, Neodymium, Wavelets, Anisotropic diffusion, Human vision and color perception
Speckle reduction in optical coherence tomography (OCT) images plays an important role in further image analysis. Although numerous despeckling methods, such as the Kuan’s filter, the Frost’s filter, wavelet based methods, anisotropic diffusion methods, have been proposed for despeckling OCT images, these methods generally tend to provide insufficient speckle suppression or limited detail preservation especially at high speckle corruption because of the insufficient utilization of image information. Different from these denoising methods, the nonlocal means (NLM) method explores nonlocal image self-similarities for image denoising, thereby providing a new method for speckle reduction in OCT images. However, the NLM method determines image self-similarities based on the intensities of noisy pixels, which will degrade its performance in restoring OCT images.
To address this problem, the Tchebichef moments based nonlocal means (TNLM) method is proposed for speckle suppression. Distinctively, he TNLM method determines the nonlocal self-similarities of the OCT images by computing the Euclidean distance between Tchebichef moments of two image patches centered at two pixels of interest in the prefiltered image. Due to the superior feature representation capability of Tchebichef moments, the proposed method can utilize more image structural information for the accurate computation of image self-similarities. The experiments on the clinical OCT images indicate that the TNLM method outperforms numerous despeckling methods in that it can suppress speckle noise more effectively while preserving image details better in terms of human vision, and it can provide higher signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), equivalent number of looks (ENL) and cross correlation (XCOR).
Optical coherence tomography (OCT) images are usually degraded by significant speckle noise, which will strongly hamper their quantitative analysis. However, speckle noise reduction in OCT images is particularly challenging because of the difficulty in differentiating between noise and the information components of the speckle pattern. To address this problem, the spiking cortical model (SCM)-based nonlocal means method is presented. The proposed method explores self-similarities of OCT images based on rotation-invariant features of image patches extracted by SCM and then restores the speckled images by averaging the similar patches. This method can provide sufficient speckle reduction while preserving image details very well due to its effectiveness in finding reliable similar patches under high speckle noise contamination. When applied to the retinal OCT image, this method provides signal-to-noise ratio improvements of >16 dB with a small 5.4% loss of similarity.
KEYWORDS: Ultrasonography, Speckle, Image processing, Denoising, Signal to noise ratio, Visualization, Medical imaging, Medical research, Lymphatic system, Liver
Despeckling of ultrasound images, as a very active topic research in medical image processing, plays an important or even indispensable role in subsequent ultrasound image processing. The non-local total variation (NLTV) method has been widely applied to denoising images corrupted by Gaussian noise, but it cannot provide satisfactory restoration results for ultrasound images corrupted by speckle noise. To address this problem, a novel non-local total variation despeckling method is proposed for speckle reduction. In the proposed method, the non-local gradient is computed on the images restored by the optimized Bayesian non-local means (OBNLM) method and it is introduced into the total variation method to suppress speckle in ultrasound images. Comparisons of the restoration performance are made among the proposed method and such state-of-the-art despeckling methods as the squeeze box filter (SBF), the non-local means (NLM) method and the OBNLM method. The quantitative comparisons based on synthetic speckled images show that the proposed method can provide higher Peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) than compared despeckling methods. The subjective visual comparisons based on synthetic and real ultrasound images demonstrate that the proposed method outperforms other compared algorithms in that it can achieve better performance of noise reduction, artifact avoidance, edge and texture preservation.
The globally fixed decay parameter is generally adopted in the traditional nonlocal means method for similarity computation, which has a negative influence on its restoration performance. To address this problem, we propose to adaptively tune the decay parameter for each image pixel using the golden section search method based on the pixel-wise minimum mean square error, which can be estimated using the prefiltered result and the estimated noise component. The quantitative and subjective comparisons of restoration performance among the proposed method and several state-of-the-art methods indicate that it can achieve a better performance in noise reduction, artifact avoidance, and detail preservation.
Images acquired in free breathing using contrast enhanced ultrasound exhibit a periodic motion that needs to be
compensated for if a further accurate quantification of the hepatic perfusion analysis is to be executed. In this work, we
present an algorithm to compensate the respiratory motion by effectively combining the PCA (Principal Component
Analysis) method and block matching method. The respiratory kinetics of the ultrasound hepatic perfusion image
sequences was firstly extracted using the PCA method. Then, the optimal phase of the obtained respiratory kinetics was
detected after normalizing the motion amplitude and determining the image subsequences of the original image
sequences. The image subsequences were registered by the block matching method using cross-correlation as the
similarity. Finally, the motion-compensated contrast images can be acquired by using the position mapping and the
algorithm was evaluated by comparing the TICs extracted from the original image sequences and compensated image
subsequences. Quantitative comparisons demonstrated that the average fitting error estimated of ROIs (region of interest)
was reduced from 10.9278 ± 6.2756 to 5.1644 ± 3.3431 after compensating.
Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided
radiotherapy, non-invasive diagnosis, and treatment planning. Although numerous researches have been done in
developing various medical image fusion algorithms, the disadvantage of these approaches is that they lack universality in dealing with different kinds of medical images. To address this problem, we have proposed a novel method of medical image fusion using the spiking cortical model (SCM) for the first time. In the paper, the mathematical model of SCM is firstly described, and then image fusion algorithm with SCM is introduced in detail. To show that the SCM based fusion method can deal with multimodal medical images, we have used three pairs of medical images with different modalities in the simulation experiments and made comparisons among the proposed method and the state-of-art fusion methods such as Laplacian pyramid, Contrast pyramid, Morphological pyramid and Ratio pyramid. The performance of various methods is investigated using such image assessment metrics as Mutual Information (MI), the edge preservation values (QAB/F), the Local Structural Similarity (LSSIM) and the Universal Image Quality Index (UIQI). The experimental results show that our proposed method outperforms other methods in both visual effect and objective evaluation. It demonstrates that the SCM based method is a highly effective method for multi-modal medical image fusion due to its versatility and stability.
Image fusion quality assessment plays a critically important role in the field of medical imaging. To evaluate image fusion quality effectively, a lot of assessment methods have been proposed. Examples include mutual information (MI), root mean square error (RMSE), and universal image quality index (UIQI). These image fusion assessment methods could not reflect the human visual inspection effectively. To address this problem, we have proposed a novel image fusion assessment method which combines the nonsubsampled contourlet transform (NSCT) with the regional mutual information in this paper. In this proposed method, the source medical images are firstly decomposed into different levels by the NSCT. Then the maximum NSCT coefficients of the decomposed directional images at each level are obtained to compute the regional mutual information (RMI). Finally, multi-channel RMI is computed by the weighted sum of the obtained RMI values at the various levels of NSCT. The advantage of the proposed method lies in the fact that the NSCT can represent image information using multidirections and multi-scales and therefore it conforms to the multi-channel characteristic of human visual system, leading to its outstanding image assessment performance. The experimental results using CT and MRI images demonstrate that the proposed assessment method outperforms such assessment methods as MI and UIQI based measure in evaluating image fusion quality and it can provide consistent results with human visual assessment.
To improve the antinoise performance of the smallest univalue segment assimilating nucleus (SUSAN) edge detector, a nonlocal means-based SUSAN edge detector is proposed. The proposed method first determines the initial SUSAN edge response based on the image patch convolved with an adaptive kernel instead of the single pixel. Then it computes the final edge response using the weighted sum of the initial edge responses of the pixels with their structures similar to the considered pixel. Extensive simulations on natural and real images demonstrate that compared with state-of-the-art detectors, the proposed method performs much better in terms of robustness to noise and edge detection and it provides significantly higher values of Pratt’s figure of merit and performance measure.
A novel noise detector based on the spiking cortical model (SCM) is proposed for switching-based filters. In the proposed noise detector, the corrupted pixels are firstly identified as noise candidates based on the firing time of the SCM, and then the misclassified noise-free pixels are dismissed from noise candidates based on the absolute difference of the firing time between the considered neurons and their neighboring neurons. Extensive simulations show that although the proposed noise detector generally has lower computational efficiency than several state-of-the-art noise detectors, it outperforms all the compared noise detectors in noise detection accuracy by classifying the pixels in the corrupted images with very few or no mistakes at the various noise ratios.
Despeckling of ultrasound images is a crucial step for facilitating subsequent image processing. The non-local means
(NLM) filter has been widely applied for denoising images corrupted by Gaussian noise. However, the direct application
of this filter in ultrasound images cannot provide satisfactory restoration results. To address this problem, a novel
iterative adaptive non-local means (IANLM) filter is proposed to despeckle ultrasound images. In the proposed filter, the
speckle noise is firstly transformed into additive Gaussian noise by square root operation. Then the decay parameter is
estimated based on a selected homogeneous region. Finally, an iterative strategy combined with the local clustering
method based on pixel intensities is adopted to realize effective image smoothing while preserving image edges.
Comparisons of the restoration performance of IANLM filter with other state-of-the-art despeckling methods are made.
The quantitative comparisons of despeckling synthetic images based on Peak signal-to-noise ratio (PSNR) show that the
IANLM filter can provide the best restoration performance among all the evaluated filters. The subjective visual
comparisons of the denoised synthetic and ultrasound images demonstrate that the IANLM filter outperforms other
compared algorithms in that it can achieve better performance of noise reduction, artifact avoidance, edges and textures
preservation and contrast enhancement.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.