In vivo needle visualization in ultrasound images using tensor-based filtering

In this paper, region-difference filters for the segmentation of liver ultrasound (US) images are proposed. Region-difference filters evaluate maximum difference of the average of two regions of the window around the center pixel. Implementing the filters on the whole image gives region-difference image. This image is then converted into binary image and morphologically operated for segmenting the desired lesion from the ultrasound image. The proposed method is compared with the maximum a posteriori-Markov random field (MAP-MRF), Chan-Vese active contour method (CV-ACM), and active contour region-scalable fitting energy (RSFE) methods. MATLAB code available online for the RSFE method is used for comparison whereas MAP-MRF and CV-ACM methods are coded in MATLAB by authors. Since no comparison is available on common database for the performance of the three methods, therefore, performance comparison of the three methods and proposed method was done on liver US images obtained from PGIMER, Chandigarh, India and from online resource. A radiologist blindly analyzed segmentation results of the 4 methods implemented on 56 images and had selected the segmentation result obtained from the proposed method as best for 46 test US images. For the remaining 10 US images, the proposed method performance was very near to the other three segmentation methods. The proposed segmentation method obtained the overall accuracy of 99.32% in comparison to the overall accuracy of 85.9, 98.71, and 68.21% obtained by MAP-MRF, CV-ACM, and RSFE methods, respectively. Computational time taken by the proposed method is 5.05s compared to the time of 26.44, 24.82, and 28.36s taken by MAP-MRF, CV-ACM, and RSFE methods, respectively.

Automatic seed selection is an important and crucial step toward the boundary detection in ultrasound B-scan images. This paper focuses on a methodological framework that can automatically detect a seed point of an ultrasound image by using texture features. Based on the selected seeds of cluster the ultrasound images are segmented using active contour, K-means and Otsu methods. The comparative analysis of these segmentation techniques is also reported. The proposed method is applied on 116 ultrasound images in which 45 are benign cases and 71 malignant cases. The quantitative experimental results show that the proposed method can successfully find an accurate seed point based on texture features and it has the ability to segment the image with high accuracy of 89.65 %. The proposed method is faster and performs more accurate segmentation than existing algorithms.

Ultrasound (US) imaging is considered as one of the most advanced diagnostic tools in medical use. However, a drawback of the medical US imaging is its poor quality of the image, which is affected by speckle noise. In this paper, a MultiLayer Back-Propagation Neural Networks (MLBPNNs) with the Epanechnikov fuzzy function is proposed to reduce the speckle, and while at the same time, enhance the lesion boundaries of the US image. The main goal of the proposed method is to improve the quality of US image so as to improve the quality of the humans interpretation and the computer systems autoedge detection. In order to automatically detect the lesion boundary by a computer system, an edge enhancement is required. Evaluating the simulation results by Peak Signal to Noise Ratio (PSNR), Normalized Mean Square Error (NMSE), Detail Variance (DV), and Background Variance (BV), the proposed method demonstrates an increased performance of reducing the speckle and enhancing the edge. The proposed method has higher PSNR than conventional methods and can remove the speckle sufficiently, so that tumor boundaries of real US breast tumor image could be preserved and detected.

The visual quality of ultrasound (US) images is crucial for clinical diagnosis and treatment. The main source of image quality degradation is the inherent speckle noise generated during US image acquisition. Current deep learning-based methods cannot preserve the maximum boundary contrast when removing noise and speckle. In this paper, we address the issue by proposing a novel wavelet-based generative adversarial network (GAN) for real-time high-quality US image reconstruction, viz. WGAN-DUS. First, we propose a batch normalization module (BNM) to balance the importance of each sub-band image and fuse sub-band features simultaneously. Then, a wavelet reconstruction module (WRM) integrated with a cascade of wavelet residual channel attention block (WRCAB) is proposed to extract distinctive sub-band features used to reconstruct denoised images. A gradual tuning strategy is proposed to fine-tune our generator for better despeckling performance. We further propose a wavelet-based discriminator and a comprehensive loss function to effectively suppress speckle noise and preserve the image features. Besides, we have designed an algorithm to estimate the noise levels during despeckling of real US images. The performance of our network was then evaluated on natural, synthetic, simulated and clinical US images and compared against various despeckling methods. To verify the feasibility of WGAN-DUS, we further extend our work to uterine fibroid segmentation with the denoised US image of the proposed approach. Experimental result demonstrates that our proposed method is feasible and can be generalized to clinical applications for despeckling of US images in real-time without losing its fine details.

Ultrasound imaging is a widely used and safe medical diagnostic technique, due to its noninvasive nature, low cost, capability of forming real time imaging, and the continuing improvements in image quality. However; the usefulness of ultrasound imaging is degraded by the presence of signal dependant noise known as speckle. It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. In ultrasound (US) imaging, denoising is intended to improve quantitative image analysis techniques. In this paper, a new version of the Non Local (NL-) means filter adapted for US images is proposed based on Similarity function depend on specific characteristics of the variance speckle noise in ultrasound images. The proposed method has been compared with Median, Wavelet, Mean and variance local statistics, Geometric, Anisotropic diffusion filtering, and Non ' local means filter using quantitative parameters. From the visual results and image quality evaluation metrics obtained over real images we can conclude that the modified(NL-) means filter can be successfully used for ultrasound image denoising, and performs better results than all other methods while still retaining the structural details and retains the edges and textures very well while removing speckle noise.

Rigid registration of intraoperative ultrasound (US) and preoperative CT image is important for providing real-time guidance during operations. However, due to the low spatial and temporal resolutions and the dissimilarity between US and CT, accurate registration of CT and US images is still a challenging problem. The authors propose an adaptive-mask-based CT and US registration method. The registration is initialized by matching the image regions of CT and US with intensity distinctiveness. The registration is a multistage iterative process in which the US region mask is adaptively updated. Each stage is an interleaving process of optimizing a global similarity energy and updating the mask of US by selecting high saliency and local statistical dependency regions. Performances of their proposed method and mutual information (MI) based method are validated with simulated, in vitro phantom and real patient datasets. Results show that their method has larger capture range in all datasets. The estimated transformation parameters in their method are more accurate than the mutual information based method. By using an adaptively updated mask of the US image, regions with salient intensity information and high intensity correlation with CT are included in the registration. Regions which have low correlation with CT such as artifacts are excluded in the registration so that the robustness and accuracy of the intensity-based registration method are improved.

Contrast enhancement of ultrasound imaging of the knee joint cartilage for early detection of knee osteoarthritis

Cerebellum measurements of routinely acquired ultrasound (US) images are commonly used to estimate gestational age and to assess structural abnormalities of the developing central nervous system. Investigating associations between the developing cerebellum and neurodevelopmental outcomes post partum requires standardized cerebellum measurements from large clinical datasets. Such investigations have the potential to identify structural changes that can be used as biomarkers to predict growth and neurodevelopmental outcomes. For this purpose, high throughput, accurate, and unbiased measurements are necessary to replace existing manual, semi-automatic, and automated approaches which are tedious and lack reproducibility and accuracy. In this study, we propose a new deep learning algorithm for automated segmentation of the fetal cerebellum from 2-dimensional (2D) US images. We propose ResU-Net-c a semantic segmentation model optimized for fetal cerebellum structure. We leverage U-Net as a base model with the integration of residual blocks (Res) and introduce dilation convolution in the last two layers to segment the cerebellum (c) from noisy US images. Our experiments used a 5-fold cross-validation with 588 images for training and 146 for testing. Our ResU-Net-c achieved a mean Dice Score Coefficient, Hausdorff Distance, Recall, and Precision of 87.00%, 28.15, 86.00%, and 90.00%, respectively. The superiority of the proposed method over the other U-Net based methods is statistically significant (p < 0.001). Our proposed method can be leveraged to enable high throughput image analysis in clinical research fetal US images and can be employed in the biometric assessment in fetal US images on a larger scale.

This paper presents a wavelet-based hybrid threshold method according to the soft- and hard-threshold functions proposed by Donoho. The wavelet-based hybrid threshold method may help doctors to know more details on the liver disease through denoising the ultrasound image of the liver. First of all, an analytical expression for the hybrid threshold function is discussed. The wavelet-based hybrid threshold method is then investigated for ultrasound image of the liver. Finally, we test the influence of this parameter on the proposed method with the real ultrasound image corrupted by speckle noise with different variances. Moreover, we compare the proposed method under the varying parameters with the soft-threshold function and the hard-threshold function. Three metrics, which are correlation coefficient, edge preservation index and structural similarity index, are measured to quantify the denoised results of ultrasound liver image. Experimental results demonstrate the potential of the proposed method for ultrasound liver image denosing.

We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to pre-process the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model). We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing state-of- the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. al. and the local mean technique proposed by Yap et. al., in proving the proposed method's robustness and accuracy.

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function. Hereto, we propose an intelligent estimator based on artificial neural networks (ANNs) to estimate the variances of noise and blur, which, in turn, are used to obtain an image without discernible distortions. A set of statistical features computed from the image and its complex wavelet sub-bands are used as input to the ANN. In the proposed method, we solve the inverse Rayleigh function numerically for speckle reduction and use the Richardson-Lucy algorithm for de-blurring. The performance of this method is compared with that of the traditional methods by applying them to a synthetic, physical phantom and clinical data, which confirms better restoration results by the proposed method.

A versatile technique for visual enhancement of medical ultrasound images

Segmenting the lesion region from 2D ultrasound (US) images is an importance step in defining the intra-operative planning of computer-assisted therapy. However, it still remains a challenge owing to two reasons: (1) The signal-to-noise ratio (SNR) of the US images is originally low, and the inhomogeneous distribution of intensity causes local fuzzy boundaries. (2) The deformation of soft tissue causes makes the geometry contour of the segmentation area complex. Because of these factors, the classical segmentation methods do not yield the desired results in US images. To solve these problems, a novel segmentation method based on a dynamics statistical shape model is proposed. In this study, first, a radial time series model was defined for use as a parametric shape model of the lesion region, and a stochastic difference equation (SDE) was used as the contour dynamics generating model; then, the coefficients of the dynamics generating model and the shape prior probability were deduced by utilizing the property of Fokker-Planck equation (FPE) and training the samples. This statistical shape model (SSM) was referred to as the SF-SSM. Furthermore, a radial profile feature model (RPFM) was developed based on the statistical features of the intensity distribution from the inside to outside of the lesion region and based on the radial time series model. The RPFM provided a likelihood probability for the prior shape. Finally, through the segmentation optimization process, the maximum posteriori process was determined. In order to verify the performance of our method, we used real clinical ultrasound images as the training and test sets. We compared the proposed model with three well-known segmentation methods for the same test set. The experimental results showed that the proposed method provided more accurate results than the other methods for different quality US images. Furthermore, the friendly initialization of the proposed method improves the efficiency of interactive segmentation, thus benefitting computer-assisted therapy.

Ultrasound (US) images are useful in medical diagnosis. US is preferred over other medical diagnosis technique because it is non-invasive in nature and has low cost. The presence of speckle noise in US images degrades its usefulness. A method that reduces the speckle noise in US images can help in correct diagnosis. This method also should preserve the important structural information in US images while removing the speckle noise. In this paper, a method for removing speckle noise using a combination of wavelet, total variation (TV) and morphological operations has been proposed. The proposed method achieves denoising by combining the advantages of the wavelet, TV and morphological operations along with the utilization of adaptive regularization parameter which controls the amount of smoothing during denoising. The work in this paper has the capability of reducing speckle noise while preserving the structural information in the denoised image. The proposed method demonstrates strong denoising for synthetic and real ultrasound images, which is also supported by the results of various quantitative measures and visual inspection.

In this study, we propose combining three-dimensional (3D) transrectal ultrasound (TRUS) and 3D transabdominal ultrasound (TAUS) images of gynecologic brachytherapy applicators to leverage the advantages of each imaging perspective, providing a broader field-of-view and allowing previously obscured features to be recovered. The aim of this study was to evaluate the feasibility of fusing these 3D ultrasound (US) perspectives based on the applicator geometry in a phantom prior to clinical implementation. In proof-of-concept experiments, 3D US images of application-specific multimodality pelvic phantoms were acquired with tandem-and-ring and tandem-and-ovoids applicators using previously validated imaging systems. Two TRUS images were acquired at different insertion depths and manually fused based on the position of the ring/ovoids to broaden the TRUS field-of-view. The phantom design allowed "abdominal thickness" to be modified to represent different body habitus and TAUS images were acquired at three thicknesses for each applicator. The merged TRUS images were then combined with TAUS images by rigidly aligning applicator components and manually refining the registration using the positions of source channels and known tandem length, as well as the ring diameter for the tandem-and-ring applicator. Combined 3D US images were manually, rigidly registered to images from a second modality (magnetic resonance (MR) imaging for the tandem-and-ring applicator and X-ray computed tomography (CT) for the tandem-and-ovoids applicator (based on applicator compatibility)) to assess alignment. Four spherical fiducials were used to calculate target registration errors (TREs), providing a metric for validating registrations, where TREs were computed using root-mean-square distances to describe the alignment of manually identified corresponding fiducials. An analysis of variance (ANOVA) was used to identify statistically significant differences (p<0.05) between the TREs for the three abdominal thicknesses for each applicator type. As an additional indicator of geometric accuracy, the bladder was segmented in the 3D US and corresponding MR/CT images, and volumetric differences and Dice similarity coefficients (DSCs) were calculated. For both applicator types, the combination of 3D TRUS with 3D TAUS images allowed image information obscured by the shadowing artifacts under single imaging perspectives to be recovered. For the tandem-and-ring applicator, the mean ± one standard deviation (SD) TREs from the images with increasing thicknesses were 1.37 ± 1.35mm, 1.84 ± 1.22mm, and 1.60 ± 1.00mm. Similarly, for the tandem-and-ovoids applicator, the mean ± SD TREs from the images with increasing thicknesses were 1.37 ± 0.35mm, 1.95 ± 0.90mm, and 1.61 ± 0.76mm. No statistically significant difference was detected in the TREs for the three thicknesses for either applicator type. The mean volume differences for the bladder segmentations were 3.14% and 2.33% and mean DSCs were 87.8% and 87.7% for the tandem-and-ring and tandem-and-ovoids applicators, respectively. In this proof-of-concept study, we demonstrated the feasibility of fusing 3D TRUS and 3D TAUS images based on the geometry of tandem-and-ring and tandem-and-ovoids applicators. This represents a step toward an accessible and low-cost 3D imaging method for gynecologic brachytherapy, with the potential to extend this approach to other intracavitary configurations and hybrid applicators.