Ultrasound image segmentation: a survey

Enclosure Transform for Interest Point Detection From Speckle Imagery

We present a fast enclosure transform (ET) to localize complex objects of interest from speckle imagery. This approach explores the spatial confinement on regional features from a sparse image feature representation. Unrelated, broken ridge features surrounding an object are organized collaboratively, giving rise to the enclosureness of the object. Three enclosure likelihood measures are constructed, consisting of the enclosure force, potential energy, and encloser count. In the transform domain, the local maxima manifest the locations of objects of interest, for which only the intrinsic dimension is known a priori. The discrete ET algorithm is computationally efficient, being on the order of O(MN) using N measuring distances across an image of M ridge pixels. It involves easy and few parameter settings. We demonstrate and assess the performance of ET on the automatic detection of the prostate locations from supra-pubic ultrasound images. ET yields superior results in terms of positive detection rate, accuracy and coverage.

Left Ventricular Myocardial Segmentation in 3-D Ultrasound Recordings: Effect of Different Endocardial and Epicardial Coupling Strategies

Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Though development of automatic endocardial segmentation methods has received much attention, the same cannot be said about epicardial segmentation, in spite of the importance of full myocardial segmentation. In this paper, different ways of coupling the endocardial and epicardial segmentations are contrasted and compared with uncoupled segmentation. For this purpose, the B-spline explicit active surfaces framework was used; 27 3-D echocardiographic images were used to validate the different coupling strategies, which were compared with manual contouring of the endocardial and epicardial borders performed by an expert. It is shown that an independent segmentation of the endocardium followed by an epicardial segmentation coupled to the endocardium is the most advantageous. In this way, a framework for fully automatic 3-D myocardial segmentation is proposed using a novel coupling strategy.

Real-Time Ultrasound Segmentation, Analysis and Visualisation of Deep Cervical Muscle Structure

Despite widespread availability of ultrasound and a need for personalised muscle diagnosis (neck/back pain-injury, work related disorder, myopathies, neuropathies), robust, online segmentation of muscles within complex groups remains unsolved by existing methods. For example, Cervical Dystonia (CD) is a prevalent neurological condition causing painful spasticity in one or multiple muscles in the cervical muscle system. Clinicians currently have no method for targeting/monitoring treatment of deep muscles. Automated methods of muscle segmentation would enable clinicians to study, target, and monitor the deep cervical muscles via ultrasound. We have developed a method for segmenting five bilateral cervical muscles and the spine via ultrasound alone, in real-time. Magnetic Resonance Imaging (MRI) and ultrasound data were collected from 22 participants (age: 29.0±6.6, male: 12). To acquire ultrasound muscle segment labels, a novel multimodal registration method was developed, involving MRI image annotation, and shape registration to MRI-matched ultrasound images, via approximation of the tissue deformation. We then applied polynomial regression to transform our annotations and textures into a mean space, before using shape statistics to generate a texture-to-shape dictionary. For segmentation, test images were compared to dictionary textures giving an initial segmentation, and then we used a customized Active Shape Model to refine the fit. Using ultrasound alone, on unseen participants, our technique currently segments a single image in [Formula: see text] to over 86% accuracy (Jaccard index). We propose this approach is applicable generally to segment, extrapolate and visualise deep muscle structure, and analyse statistical features online.

Automatic gallbladder and gallstone regions segmentation in ultrasound image

PURPOSE

As gallbladder diseases including gallstone and cholecystitis are mainly diagnosed by using ultra-sonographic examinations, we propose a novel method to segment the gallbladder and gallstones in ultrasound images.

METHODS

The method is divided into five steps. Firstly, a modified Otsu algorithm is combined with the anisotropic diffusion to reduce speckle noise and enhance image contrast. The Otsu algorithm separates distinctly the weak edge regions from the central region of the gallbladder. Secondly, a global morphology filtering algorithm is adopted for acquiring the fine gallbladder region. Thirdly, a parameter-adaptive pulse-coupled neural network (PA-PCNN) is employed to obtain the high-intensity regions including gallstones. Fourthly, a modified region-growing algorithm is used to eliminate physicians' labeled regions and avoid over-segmentation of gallstones. It also has good self-adaptability within the growth cycle in light of the specified growing and terminating conditions. Fifthly, the smoothing contours of the detected gallbladder and gallstones are obtained by the locally weighted regression smoothing (LOESS).

RESULTS

We test the proposed method on the clinical data from Gansu Provincial Hospital of China and obtain encouraging results. For the gallbladder and gallstones, average similarity percent of contours (EVA) containing metrics dice's similarity , overlap fraction and overlap value is 86.01 and 79.81%, respectively; position error is 1.7675 and 0.5414 mm, respectively; runtime is 4.2211 and 0.6603 s, respectively. Our method then achieves competitive performance compared with the state-of-the-art methods.

CONCLUSIONS

The proposed method is potential to assist physicians for diagnosing the gallbladder disease rapidly and effectively.

Evaluation and comparison of current biopsy needle localization and tracking methods using 3D ultrasound

This article compares four different biopsy needle localization algorithms in both 3D and 4D situations to evaluate their accuracy and execution time. The localization algorithms were: Principle component analysis (PCA), random Hough transform (RHT), parallel integral projection (PIP) and ROI-RK (ROI based RANSAC and Kalman filter). To enhance the contrast of the biopsy needle and background tissue, a line filtering pre-processing step was implemented. To make the PCA, RHT and PIP algorithms comparable with the ROI-RK method, a region of interest (ROI) strategy was added. Simulated and ex-vivo data were used to evaluate the performance of the different biopsy needle localization algorithms. The resolutions of the sectorial and cylindrical volumes were 0.3mm×0.4mm×0.6mmand0.1mm×0.1mm×0.2mm (axial×lateral×azimuthal) respectively. In so far as the simulation and experimental results show, the ROI-RK method successfully located and tracked the biopsy needle in both 3D and 4D situations. The tip localization error was within 1.5mm and the axis accuracy was within 1.6mm. To the best of our knowledge, considering both localization accuracy and execution time, the ROI-RK was the most stable and time-saving method. Normally, accuracy comes at the expense of time. However, the ROI-RK method was able to locate the biopsy needle with high accuracy in real time, which makes it a promising method for clinical applications.

Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review

Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis.

Simultaneous Analysis of 2D Echo Views for Left Atrial Segmentation and Disease Detection

We propose a joint information approach for automatic analysis of 2D echocardiography (echo) data. The approach combines a priori images, their segmentations and patient diagnostic information within a unified framework to determine various clinical parameters, such as cardiac chamber volumes, and cardiac disease labels. The main idea behind the approach is to employ joint Independent Component Analysis of both echo image intensity information and corresponding segmentation labels to generate models that jointly describe the image and label space of echo patients on multiple apical views, instead of independently. These models are then both used for segmentation and volume estimation of cardiac chambers such as the left atrium and for detecting pathological abnormalities such as mitral regurgitation. We validate the approach on a large cohort of echoes obtained from 6,993 studies. We report performance of the proposed approach in estimation of the left-atrium volume and detection of mitral-regurgitation severity. A correlation coefficient of 0.87 was achieved for volume estimation of the left atrium when compared to the clinical report. Moreover, we classified patients that suffer from moderate or severe mitral regurgitation with an average accuracy of 82%.

Left ventricle segmentation in fetal echocardiography using a multi-texture active appearance model based on the steered Hermite transform

OBJECTIVE

Fetal echocardiographic analysis is essential for detecting cardiac defects at early gestational ages. Fetal cardiac function can be assessed by performing some measurements regarding the dimension and shape of the heart cavities. In this work we propose an automatic segmentation method applied to the analysis of the left ventricle in fetal echocardiography.

METHODS

For segmentation of the left ventricle, we designed a novel multi-texture active appearance model (AAM) based on the Hermite transform (HT). Local orientation analysis is addressed by steering the coefficients obtained with the HT. The method basically consists of an AAM-based scheme which uses the steered HT to efficiently code texture patterns of the input image. A wider and detailed description of the image features can be obtained with this method. Compared with classic AAM methods, the segmentation performance is substantially improved with the proposed scheme. Since AAM-based approaches process local information, an automatic method is also proposed to initialize the multi-texture AAM. For this purpose, a database of pre-segmented images was built. Then, techniques such as thresholding, mathematical morphology and correlation are combined to identify the position and orientation of the left ventricle. Typical issues found in fetal cardiac ultrasound images such as different orientations and shape variations of the heart cavities can be easily handled with the designed method.

RESULTS

Several images of fetal echocardiography were used to evaluate the proposed segmentation method. The algorithm performance was validated using different metrics. We used a database of 143 real images of fetal hearts acquired for different phases of the cardiac cycle. We obtained an average Dice coefficient of 0.8631 and a point-to-curve distance of 2.027 pixels. The proposed algorithm was also validated by comparing it with other segmentation methods.

CONCLUSIONS

We have designed an automatic algorithm for left ventricle segmentation in fetal echocardiography. The reported results demonstrate that the proposed approach can achieve an efficient segmentation of the left ventricular cavity. Typical problems found in images of fetal echocardiography are satisfactorily handled with the proposed multi-texture AAM scheme.

Image Segmentation and Machine Learning for Detection of Abdominal Free Fluid in Focused Assessment With Sonography for Trauma Examinations: A Pilot Study

The objective of this pilot study was to test the feasibility of automating the detection of abdominal free fluid in focused assessment with sonography for trauma (FAST) examinations. Perihepatic views from 10 FAST examinations with positive results and 10 FAST examinations with negative results were used. The sensitivity and specificity compared to manual classification by trained physicians was evaluated. The sensitivity and specificity (95% confidence interval) were 100% (69.2%-100%) and 90.0% (55.5%-99.8%), respectively. These findings suggest that computerized detection of free fluid on abdominal ultrasound images may be sensitive and specific enough to aid clinicians in their interpretation of a FAST examination.

Renal Segmentation From 3D Ultrasound via Fuzzy Appearance Models and Patient-Specific Alpha Shapes

Ultrasound (US) imaging is the primary imaging modality for pediatric hydronephrosis, which manifests as the dilation of the renal collecting system (CS). In this paper, we present a new framework for the segmentation of renal structures, kidney and CS, from 3DUS scans. First, the kidney is segmented using an active shape model-based approach, tailored to deal with the challenges raised by US images. A weighted statistical shape model allows to compensate the image variation with the propagation direction of the US wavefront. The model is completed with a new fuzzy appearance model and a multi-scale omnidirectional Gabor-based appearance descriptor. Next, the CS is segmented using an active contour formulation, which combines contour- and intensity-based terms. The new positive alpha detector presented here allows to control the propagation process by means of a patient-specific stopping function created from the bands of adipose tissue within the kidney. The performance of the new segmentation approach was evaluated on a dataset of 39 cases, showing an average Dice's coefficient of 0.86±0.05 for the kidney, and 0.74 ± 0.10 for the CS segmentation, respectively. These promising results demonstrate the potential utility of this framework for the US-based assessment of the severity of pediatric hydronephrosis.

Automatic fetal measurements for low-cost settings by using Local Phase Bone detection

The estimation of gestational age is done mostly by measurements of fetal anatomical structures such as the head and femur. These measurement are also used in diagnosis and growth assessment. Manual measurements is operator dependent and hence subject to variability.

Speckle noise removal applied to ultrasound image of carotid artery based on total least squares model

An ultrasonic image speckle noise removal method by using total least squares model is proposed and applied onto images of cardiovascular structures such as the carotid artery. On the basis of the least squares principle, the related principle of minimum square method is applied to cardiac ultrasound image speckle noise removal process to establish the model of total least squares, orthogonal projection transformation processing is utilized for the output of the model, and the denoising processing for the cardiac ultrasound image speckle noise is realized. Experimental results show that the improved algorithm can greatly improve the resolution of the image, and meet the needs of clinical medical diagnosis and treatment of the cardiovascular system for the head and neck. Furthermore, the success in imaging of carotid arteries has strong implications in neurological complications such as stroke.

Visual Quality Enhancement in Optoacoustic Tomography Using Active Contour Segmentation Priors

Segmentation of biomedical images is essential for studying and characterizing anatomical structures as well as for detection and evaluation of tissue pathologies. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities in the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.

Local Transverse-Slice-Based Level-Set Method for Segmentation of 3-D High-Frequency Ultrasonic Backscatter From Dissected Human Lymph Nodes

OBJECTIVE

To detect metastases in freshly excised human lymph nodes (LNs) using three-dimensional (3-D), high-frequency, quantitative ultrasound (QUS) methods, the LN parenchyma (LNP) must be segmented to preclude QUS analysis of data in regions outside the LNP and to compensate ultrasound attenuation effects due to overlying layers of LNP and residual perinodal fat (PNF).

METHODS

After restoring the saturated radio-frequency signals from PNF using an approach based on smoothing cubic splines, the three regions, i.e., LNP, PNF, and normal saline (NS), in the LN envelope data are segmented using a new, automatic, 3-D, three-phase, statistical transverseslice-based level-set (STS-LS) method that amends Lankton's method. Due to ultrasound attenuation and focusing effects, the speckle statistics of the envelope data vary with imaged depth. Thus, to mitigate depth-related inhomogeneity effects, the STS-LS method employs gamma probabilitydensity functions to locally model the speckle statistics within consecutive transverse slices.

RESULTS

Accurate results were obtained on simulated data. On a representative dataset of 54 LNs acquired from colorectal-cancer patients, the Dice similarity coefficient for LNP, PNF, and NS were 0.938 ± 0.025, 0.832 ± 0.086, and 0.968 ± 0.008, respectively, when compared to expert manual segmentation.

CONCLUSION

The STS-LS outperforms the established methods based on global and local statistics in our datasets and is capable of accurately handling the depth-dependent effects due to attenuation and focusing.

SIGNIFICANCE

This advance permits the automatic QUS-based cancer detection in the LNs. Furthermore, the STS-LS method could potentially be used in a wide range of ultrasound-imaging applications suffering from depth-dependent effects.

Investigating the Effectiveness of Wavelet Approximations in Resizing Images for Ultrasound Image Classification

Images are difficult to classify and annotate but the availability of digital image databases creates a constant demand for tools that automatically analyze image content and describe it with either a category or a set of variables. Ultrasound Imaging is very popular and is widely used to see the internal organ(s) condition of the patient. The main target of this research is to develop a robust image processing techniques for a better and more accurate medical image retrieval and categorization. This paper looks at an alternative to feature extraction for image classification such as image resizing technique. A new mean for image resizing using wavelet transform is proposed. Results, using real medical images, have shown the effectiveness of the proposed technique for classification task comparing to bi-cubic interpolation and feature extraction.

Dense motion field estimation from myocardial boundary displacements

Minimally invasive cardiovascular interventions guided by multiple imaging modalities are rapidly gaining clinical acceptance for the treatment of several cardiovascular diseases. These images are typically fused with richly detailed pre-operative scans through registration techniques, enhancing the intra-operative clinical data and easing the image-guided procedures. Nonetheless, rigid models have been used to align the different modalities, not taking into account the anatomical variations of the cardiac muscle throughout the cardiac cycle. In the current study, we present a novel strategy to compensate the beat-to-beat physiological adaptation of the myocardium. Hereto, we intend to prove that a complete myocardial motion field can be quickly recovered from the displacement field at the myocardial boundaries, therefore being an efficient strategy to locally deform the cardiac muscle. We address this hypothesis by comparing three different strategies to recover a dense myocardial motion field from a sparse one, namely, a diffusion-based approach, thin-plate splines, and multiquadric radial basis functions. Two experimental setups were used to validate the proposed strategy. First, an in silico validation was carried out on synthetic motion fields obtained from two realistic simulated ultrasound sequences. Then, 45 mid-ventricular 2D sequences of cine magnetic resonance imaging were processed to further evaluate the different approaches. The results showed that accurate boundary tracking combined with dense myocardial recovery via interpolation/diffusion is a potentially viable solution to speed up dense myocardial motion field estimation and, consequently, to deform/compensate the myocardial wall throughout the cardiac cycle. Copyright © 2015 John Wiley & Sons, Ltd.

Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model Based on Generalized Gaussian Priors

This paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit heavy characteristic speckle patterns correlated with the tissue structures. The generalized Gaussian distribution (GGD) has been shown to be one of the most relevant distributions for characterizing the speckle in US images. Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution. The Bayesian estimators of the unknown model parameters, including the US image, the label map, and all the hyperparameters are difficult to be expressed in a closed form. Thus, we investigate a Gibbs sampler to generate samples distributed according to the posterior of interest. These generated samples are finally used to compute the Bayesian estimators of the unknown parameters. The performance of the proposed Bayesian model is compared with the existing approaches via several experiments conducted on realistic synthetic data and in vivo US images.

Segmentation of the metacarpus and phalange in musculoskeletal ultrasound images using local active contours

This work presents a method for the automatic segmentation of metacarpus and phalange bones in ultrasound images of the second metacarpophalangeal joint (MCPJ) using Active Contours. The MCPJ is known to be the one of the first structures to be affected by rheumatic diseases like rheumatoid arthritis. The early detection and follow-up of this disease is important to prevent irreversible damage of the joints, which occurs continuously and faster if no treatment is used. To our knowledge, there is no automatic system to quantify the extension of the lesions resulting from rheumatic activity. The objective of this work is to identify the metacarpus and the phalange bones using local active contours. To our knowledge, there is no well established method for this problem and this technique has not been used yet in these structures. Results proved that the automatic segmentation is possible with an error of 3 pixels for a confidence of 80%.

A probabilistic framework based on SLIC-superpixel and Gaussian processes for segmenting nerves in ultrasound images

We deal with an important problem in the field of anesthesiology known as automatic segmentation of nerve structures depicted in ultrasound images. This is important to aid the experts in anesthesiology, in order to carry out Peripheral Nerve Blocking (PNB). Ultrasound imaging has gained recent interest for performing PNB procedures since it offers a non-invasive visualization of the nerve and the anatomical structures around it. However, the location of these nerves in ultrasound images is a difficult task for the specialist due to the artifacts (i.e. speckle noise) that affect the intelligibility of a given image. In this paper, we present a probabilistic approach based on Simple Linear Iterative Clustering (SLIC-superpixels) and Gaussian processes for automatic segmentation of peripheral nerves. First, we use Graph cuts segmentation to define a region of interest (ROI). Such a ROI is divided into several correlated regions using SLIC-superpixels, then, a nonlinear Wavelet transform is applied as feature extraction stage. Finally, we use a classification scheme based on Gaussian Processes in order to predict the label of each parametrized superpixel (the label can be "nerve" or "background"). The accuracy of the proposed method is measured in terms of the Dice coefficient. Results obtained show performances with a Dice coefficient of 0.6524±0.0085 which brings accurate performances in nerve segmentation processes.

Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings

A plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred to as 3D speckle tracking techniques, are available from academia and industry. Although the benefits of single methods over alternative ones have been reported in separate publications, the intrinsic differences in the data and definitions used makes it hard to compare the relative performance of different solutions. To address this issue, we have recently proposed a framework to simulate realistic 3D echocardiographic recordings and used it to generate a common set of ground-truth data for 3D speckle tracking algorithms, which was made available online. The aim of this study was therefore to use the newly developed database to contrast non-commercial speckle tracking solutions from research groups with leading expertise in the field. The five techniques involved cover the most representative families of existing approaches, namely block-matching, radio-frequency tracking, optical flow and elastic image registration. The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the obtained strain measurements to diagnose pathology was also tested for ischemia and dyssynchrony.

Phase-aware echocardiogram stabilization using keyframes

This paper presents an echocardiogram stabilization method designed to compensate for unwanted auxilliary motion. Echocardiograms contain both deformable cardiac motion and approximately rigid motion due to a number of factors. The goal of this work is to stabilize the video, while preserving the informative deformable cardiac motion. Our approach incorporates synchronized side information, extracted from electrocardiography (ECG), which provides a proxy for cardiac phase. To avoid the computational expense of pairwise alignment, we propose an efficient strategy for keyframe selection, formulated as a submodular optimization problem. We evaluate our approach quantitatively on synthetic data and demonstrate its benefit as a preprocessing step for two common echocardiogram applications: denoising and left ventricle segmentation. In both cases, preprocessing with our method improved the performance compared to no preprocessing or other alignment approaches.

Automatic Measurement of Thalamic Diameter in 2-D Fetal Ultrasound Brain Images Using Shape Prior Constrained Regularized Level Sets

We derived an automated algorithm for accurately measuring the thalamic diameter from 2-D fetal ultrasound (US) brain images. The algorithm overcomes the inherent limitations of the US image modality: nonuniform density; missing boundaries; and strong speckle noise. We introduced a "guitar" structure that represents the negative space surrounding the thalamic regions. The guitar acts as a landmark for deriving the widest points of the thalamus even when its boundaries are not identifiable. We augmented a generalized level-set framework with a shape prior and constraints derived from statistical shape models of the guitars; this framework was used to segment US images and measure the thalamic diameter. Our segmentation method achieved a higher mean Dice similarity coefficient, Hausdorff distance, specificity, and reduced contour leakage when compared to other well-established methods. The automatic thalamic diameter measurement had an interobserver variability of -0.56 ± 2.29 mm compared to manual measurement by an expert sonographer. Our method was capable of automatically estimating the thalamic diameter, with the measurement accuracy on par with clinical assessment. Our method can be used as part of computer-assisted screening tools that automatically measure the biometrics of the fetal thalamus; these biometrics are linked to neurodevelopmental outcomes.

ULTRASOUND SPECTROSCOPY

We introduce the concept of "Ultrasound Spectroscopy". The premise of ultrasound spectroscopy is that by acquiring ultrasound RF data at multiple power and frequency settings, a rich set of features can be extracted from that RF data and used to characterize the underlying tissues. This is beneficial for a variety of problems, such as accurate tissue classification, application-specific image generation, and numerous other quantitative tasks. These capabilities are particularly relevant to point-of-care ultrasound (POCUS) applications, where operator experience with ultrasound may be limited. Instead of displaying B-mode images, a POCUS application using ultrasound spectroscopy can, for example, automatically detect internal abdominal bleeding. In this paper, we present ex vivo tissue phantom studies to demonstrate the accuracy of ultrasound spectroscopy over previous approaches. Our studies suggest that ultrasound spectroscopy provides exceptional accuracy and informative features for classifying blood versus other tissues across image locations and body habitus.

Contour Tracking with a Spatio-Temporal Intensity Moment

Standard edge detection operators such as the Laplacian of Gaussian and the gradient of Gaussian can be used to track contours in image sequences. When using edge operators, a contour, which is determined on a frame of the sequence, is simply used as a starting contour to locate the nearest contour on the subsequent frame. However, the strategy used to look for the nearest edge points may not work when tracking contours of non isolated gray level discontinuities. In these cases, strategies derived from the optical flow equation, which look for similar gray level distributions, appear to be more appropriate since these can work with a lower frame rate than that needed for strategies based on pure edge detection operators. However, an optical flow strategy tends to propagate the localization errors through the sequence and an additional edge detection procedure is essential to compensate for such a drawback. In this paper a spatio-temporal intensity moment is proposed which integrates the two basic functions of edge detection and tracking.

Fully automatic prostate segmentation from transrectal ultrasound images based on radial bas-relief initialization and slice-based propagation

Prostate segmentation from transrectal ultrasound (TRUS) images plays an important role in the diagnosis and treatment planning of prostate cancer. In this paper, a fully automatic slice-based segmentation method was developed to segment TRUS prostate images. The initial prostate contour was determined using a novel method based on the radial bas-relief (RBR) method, and a false edge removal algorithm proposed here in. 2D slice-based propagation was used in which the contour on each image slice was deformed using a level-set evolution model, which was driven by edge-based and region-based energy fields generated by dyadic wavelet transform. The optimized contour on an image slice propagated to the adjacent slice, and subsequently deformed using the level-set model. The propagation continued until all image slices were segmented. To determine the initial slice where the propagation began, the initial prostate contour was deformed individually on each transverse image. A method was developed to self-assess the accuracy of the deformed contour based on the average image intensity inside and outside of the contour. The transverse image on which highest accuracy was attained was chosen to be the initial slice for the propagation process. Evaluation was performed for 336 transverse images from 15 prostates that include images acquired at mid-gland, base and apex regions of the prostates. The average mean absolute difference (MAD) between algorithm and manual segmentations was 0.79±0.26mm, which is comparable to results produced by previously published semi-automatic segmentation methods. Statistical evaluation shows that accurate segmentation was not only obtained at the mid-gland, but also at the base and apex regions.

Automated Estimation of Melanocytic Skin Tumor Thickness by Ultrasonic Radiofrequency Data

OBJECTIVES

High-frequency (>20-MHz) ultrasound (US) is a noninvasive preoperative tool for assessment of melanocytic skin tumor thickness. Ultrasonic melanocytic skin tumor thickness estimation is not always easy and is related to the experience of the clinician. In this article, we present an automated thickness measurement method based on time-frequency analysis of US radiofrequency signals.

METHODS

The study was performed on 52 thin (≤1-mm) melanocytic skin tumors (46 melanocytic nevi and 6 melanomas). Radiofrequency signals were obtained with a single-element focused transducer (fundamental frequency, 22 MHz; bandwidth, 12-28 MHz). The radiofrequency data were analyzed in the time-frequency domain to make the tumor boundaries more noticeable. The thicknesses of the tumors were evaluated by 3 different metrics: histologically measured Breslow thickness, manually measured US thickness, and automatically measured US thickness.

RESULTS

The results showed a higher correlation coefficient between the automatically measured US thickness and Breslow thickness (r= 0.83; P< .0001) than the manually measured US thickness (r = 0.68; P < .0001). The sensitivity of the automated tumor thickness measurement algorithm was 96.55%, and the specificity was 78.26% compared with histologic measurement. The sensitivity of the manually measured US thickness was 75.86%, and the specificity was 73.91%.

CONCLUSIONS

The efficient automated tumor thickness measurement method developed could be used as a tool for preoperative assessment of melanocytic skin tumor thickness.

3D Transrectal Ultrasound (TRUS) Prostate Segmentation Based on Optimal Feature Learning Framework

We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning framework. Patient-specific anatomical features are extracted from aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified by the feature selection process to train the kernel support vector machine (KSVM). The well-trained SVM was used to localize the prostate of the new patient. Our segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentations (gold standard). The mean volume Dice overlap coefficient was 89.7%. In this study, we have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentations.

Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models

This work investigates how to automatically classify Focal Liver Lesions (FLLs) into three specific benign or malignant types in Contrast-Enhanced Ultrasound (CEUS) videos, and aims at providing a computational framework to assist clinicians in FLL diagnosis. The main challenge for this task is that FLLs in CEUS videos often show diverse enhancement patterns at different temporal phases. To handle these diverse patterns, we propose a novel structured model, which detects a number of discriminative Regions of Interest (ROIs) for the FLL and recognize the FLL based on these ROIs. Our model incorporates an ensemble of local classifiers in the attempt to identify different enhancement patterns of ROIs, and in particular, we make the model reconfigurable by introducing switch variables to adaptively select appropriate classifiers during inference. We formulate the model learning as a non-convex optimization problem, and present a principled optimization method to solve it in a dynamic manner: the latent structures (e.g. the selections of local classifiers, and the sizes and locations of ROIs) are iteratively determined along with the parameter learning. Given the updated model parameters in each step, the data-driven inference is also proposed to efficiently determine the latent structures by using the sequential pruning and dynamic programming method. In the experiments, we demonstrate superior performances over the state-of-the-art approaches. We also release hundreds of CEUS FLLs videos used to quantitatively evaluate this work, which to the best of our knowledge forms the largest dataset in the literature. Please find more information at "http://vision.sysu.edu.cn/projects/fllrecog/".

Postprocessing Approaches for the Improvement of Cardiac Ultrasound B-Mode Images: A Review

The improvement in the quality and diagnostic value of ultrasound images has been an ongoing research theme for the last three decades. Cardiac ultrasound suffers from a wide range of artifacts such as acoustic noise, shadowing, and enhancement. Most artifacts are a consequence of the interaction of the transmitted ultrasound signals with anatomic structures of the examined body. Structures such as bone, lungs (air), and fat have a direct limiting effect on the quality of the acquired images. Furthermore, physical phenomena such as speckle introduce a granular pattern on the imaged tissue structures that can sometimes obscure fine anatomic detail. Over the years, numerous studies have attempted to address a range of artifacts in medical ultrasound, including cardiac ultrasound B-mode images. This review provides extensive coverage of such attempts identifying their limitations as well as future research opportunities.

Multi-scale texture-based level-set segmentation of breast B-mode images

Automatic segmentation of ultrasonographic breast lesions is very challenging, due to the lesions' spiculated nature and the variance in shape and texture of the B-mode ultrasound images. Many studies have tried to answer this challenge by applying a variety of computational methods including: Markov random field, artificial neural networks, and active contours and level-set techniques. These studies focused on creating an automatic contour, with maximal resemblance to a manual contour, delineated by a trained radiologist. In this study, we have developed an algorithm, designed to capture the spiculated boundary of the lesion by using the properties from the corresponding ultrasonic image. This is primarily achieved through a unique multi-scale texture identifier (inspired by visual system models) integrated in a level-set framework. The algorithm׳s performance has been evaluated quantitatively via contour-based and region-based error metrics. We compared the algorithm-generated contour to a manual contour delineated by an expert radiologist. In addition, we suggest here a new method for performance evaluation where corrections made by the radiologist replace the algorithm-generated (original) result in the correction zones. The resulting corrected contour is then compared to the original version. The evaluation showed: (1) Mean absolute error of 0.5 pixels between the original and the corrected contour; (2) Overlapping area of 99.2% between the lesion regions, obtained by the algorithm and the corrected contour. These results are significantly better than those previously reported. In addition, we have examined the potential of our segmentation results to contribute to the discrimination between malignant and benign lesions.

An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier

In this paper, a novel framework of computer-aided diagnosis (CAD) system has been presented for the classification of benign/malignant breast tissues. The properties of the generalized pseudo-Zernike moments (GPZM) and pseudo-Zernike moments (PZM) are utilized as suitable texture descriptors of the suspicious region in the mammogram. An improved classifier- adaptive differential evolution wavelet neural network (Ada-DEWNN) is proposed to improve the classification accuracy of the CAD system. The efficiency of the proposed system is tested on mammograms from the Mammographic Image Analysis Society (mini-MIAS) database using the leave-one-out cross validation as well as on mammograms from the Digital Database for Screening Mammography (DDSM) database using 10-fold cross validation. The proposed method on MIAS-database attains a fair accuracy of 0.8938 and AUC of 0.935 (95 % CI = 0.8213-0.9831). The proposed method is also tested for in-plane rotation and found to be highly rotation invariant. In addition, the proposed classifier is tested and compared with some well-known existing methods using receiver operating characteristic (ROC) analysis using DDSM- database. It is concluded the proposed classifier has better area under the curve (AUC) (0.9289) and highly précised with 95 % CI, 0.8216 to 0.9834 and 0.0384 standard error.

Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images

Background

Anterior talofibular ligament (ATFL) is considered as the weakest ankle ligament that is most prone to injuries. Ultrasound imaging with its portable, non-invasive and non-ionizing radiation nature is increasingly being used for ATFL diagnosis. However, diagnosis of ATFL injuries requires its segmentation from ultrasound images that is a challenging task due to the existence of homogeneous intensity regions, homogeneous textures and low contrast regions in ultrasound images. To address these issues, this research has developed an efficient ATFL segmentation framework that would contribute to accurate and efficient diagnosis of ATFL injuries for clinical evaluation.

Methods

The developed framework comprises of five computational steps to segment the ATFL ligament region. Initially, region of interest is selected from the original image, which is followed by the adaptive histogram equalization to enhance the contrast level of the ultrasound image. The enhanced contrast image is further optimized by the particle swarm optimization algorithm. Thereafter, the optimized image is processed by the Chan–Vese method to extract the ATFL region through curve evolution; then the resultant image smoothed by morphological operation. The algorithm is tested on 25 subjects’ datasets and the corresponding performance metrics are evaluated to demonstrate its clinical applicability.

Results

The performance of the developed framework is evaluated based on various measurement metrics. It was found that estimated computational performance of the developed framework is 12 times faster than existing Chan–Vese method. Furthermore, the developed framework yielded the average sensitivity of 98.3 %, specificity of 96.6 % and accuracy of 96.8 % as compared to the manual segmentation. In addition, the obtained distance using Hausdorff is 14.2 pixels and similarity index by Jaccard is 91 %, which are indicating the enhanced performance whilst segmented area of ATFL region obtained from five normal (average Pixels—16,345.09), five tear (average Pixels—14,940.96) and five thickened (average Pixels—12,179.20) subjects’ datasets show good performance of developed framework to be used in clinical practices.

Conclusions

On the basis of obtained results, the developed framework is computationally more efficient and more accurate with lowest rate of coefficient of variation (less than 5 %) that indicates the highest clinical significance of this research in the assessment of ATFL injuries.

Automated Assessment of Right Ventricular Volumes and Function Using Three-Dimensional Transesophageal Echocardiography

Assessment of right ventricular (RV) function is known to be of diagnostic value in patients with RV dysfunction. Because of its complex anatomic shape, automated determination of the RV volume is difficult and strong reliance on geometric assumptions is not desired. A method for automated RV assessment was developed using three-dimensional (3-D) echocardiography without relying on a priori knowledge of the cardiac anatomy. A 3-D adaptive filtering technique that optimizes the discrimination between blood and myocardium was applied to facilitate endocardial border detection. Filtered image data were incorporated in a segmentation model to automatically detect the endocardial RV border. End-systolic and end-diastolic RV volumes, as well as ejection fraction, were computed from the automatically segmented endocardial surfaces and compared against reference volumes manually delineated by two expert cardiologists. The results reported good performance in terms of correlation and agreement with the results from the reference volumes.

Automated 3D ultrasound image segmentation to aid breast cancer image interpretation

Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

Quantification of kidneys from 3D ultrasound in pediatric hydronephrosis

This paper introduces a complete framework for the quantification of renal structures (parenchyma, and collecting system) in 3D ultrasound (US) images. First, the segmentation of the kidney is performed using Gabor-based appearance models (GAM), a variant of the popular active shape models, properly tailored to the imaging physics of US image data. The framework also includes a new graph-cut based method for the segmentation of the collecting system, including brightness and contrast normalization, and positional prior information. The significant advantage (p = 0.03) of the new method over previous approaches in terms of segmentation accuracy has been successfully verified on clinical 3DUS data from pediatric cases with hydronephrosis. The promising results obtained in the estimation of the volumetric hydronephrosis index demonstrate the potential of our new framework to quantify anatomy in US and asses the severity of hydronephrosis.

Automatic segmentation of nerve structures in ultrasound images using Graph Cuts and Gaussian processes

Peripheral Nerve Blocking (PNB), is a procedure used for performing regional anesthesia, that comprises the administration of anesthetic in the proximity of a nerve. Several techniques have been used with the purpose of locating nerve structures when the PNB procedure is performed: anatomical surface landmarks, elicitation of paresthesia, nerve stimulation and ultrasound imaging. Among those, ultrasound imaging has gained great attention because it is not invasive and offers an accurate location of the nerve and the structures around it. However, the segmentation of nerve structures in ultrasound images is a difficult task for the specialist, since such images are affected by echo perturbations and speckle noise. The development of systems for the automatic segmentation of nerve structures can aid the specialist for locating nerve structures accurately. In this paper we present a methodology for the automatic segmentation of nerve structures in ultrasound images. An initial step is carried out using Graph Cut segmentation in order to generate regions of interest; we then use machine learning techniques with the aim of segmenting the nerve structure; here, a specific non-linear Wavelet transform is used for the feature extraction stage, and Gaussian processes for the classification step. The methodology performance is measured in terms of accuracy and the dice coefficient. Results show that the implemented methodology can be used for automatically segmenting nerve structures.

Carotid ultrasound segmentation using radio-frequency derived phase information and gabor filters

Ultrasound image segmentation is a field which has garnered much interest over the years. This is partially due to the complexity of the problem, arising from the lack of contrast between different tissue types which is quite typical of ultrasound images. Recently, segmentation techniques which treat RF signal data have also become popular, particularly with the increasing availability of such data from open-architecture machines. It is believed that RF data provides a rich source of information whose integrity remains intact, as opposed to the loss which occurs through the signal processing chain leading to Brightness Mode Images. Furthermore, phase information contained within RF data has not been studied in much detail, as the nature of the information here appears to be mostly random. In this work however, we show that phase information derived from RF data does elicit structure, characterized by texture patterns. Texture segmentation of this data permits the extraction of rough, but well localized, carotid boundaries. We provide some initial quantitative results, which report the performance of the proposed technique.