Ultrasound image segmentation: a survey

A shape-optimized framework for kidney segmentation in ultrasound images using NLTV denoising and DRLSE

BACKGROUND

Computer-assisted surgical navigation aims to provide surgeons with anatomical target localization and critical structure observation, where medical image processing methods such as segmentation, registration and visualization play a critical role. Percutaneous renal intervention plays an important role in several minimally-invasive surgeries of kidney, such as Percutaneous Nephrolithotomy (PCNL) and Radio-Frequency Ablation (RFA) of kidney tumors, which refers to a surgical procedure where access to a target inside the kidney by a needle puncture of the skin. Thus, kidney segmentation is a key step in developing any ultrasound-based computer-aided diagnosis systems for percutaneous renal intervention.

METHODS

In this paper, we proposed a novel framework for kidney segmentation of ultrasound (US) images combined with nonlocal total variation (NLTV) image denoising, distance regularized level set evolution (DRLSE) and shape prior. Firstly, a denoised US image was obtained by NLTV image denoising. Secondly, DRLSE was applied in the kidney segmentation to get binary image. In this case, black and white region represented the kidney and the background respectively. The last stage is that the shape prior was applied to get a shape with the smooth boundary from the kidney shape space, which was used to optimize the segmentation result of the second step. The alignment model was used occasionally to enlarge the shape space in order to increase segmentation accuracy. Experimental results on both synthetic images and US data are given to demonstrate the effectiveness and accuracy of the proposed algorithm.

RESULTS

We applied our segmentation framework on synthetic and real US images to demonstrate the better segmentation results of our method. From the qualitative results, the experiment results show that the segmentation results are much closer to the manual segmentations. The sensitivity (SN), specificity (SP) and positive predictive value (PPV) of our segmentation result can reach 95%, 96% and 91% respectively; As well as we compared our results with the edge-based level set and level set with shape prior method by means of the same quantitative index, such as SN, SP, PPV, which have corresponding values of 97%, 88%, 78% and 81%, 91%, 80% respectively.

CONCLUSIONS

We have found NLTV denosing method is a good initial process for the ultrasound segmentation. This initial process can make us use simple segmentation method to get satisfied results. Furthermore, we can get the final segmentation results with smooth boundary by using the shape prior after the segmentation process. Every step enjoy simple energy model and every step in this framework is needed to keep a good robust and convergence property.

A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images

Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft tissue contrast highlights large variability in shape, size and texture information inside the prostate. In contrast poor soft tissue contrast between prostate and surrounding tissues in computed tomography (CT) images pose a challenge in accurate prostate segmentation. This article reviews the methods developed for prostate gland segmentation TRUS, MR and CT images, the three primary imaging modalities that aids prostate cancer diagnosis and treatment. The objective of this work is to study the key similarities and differences among the different methods, highlighting their strengths and weaknesses in order to assist in the choice of an appropriate segmentation methodology. We define a new taxonomy for prostate segmentation strategies that allows first to group the algorithms and then to point out the main advantages and drawbacks of each strategy. We provide a comprehensive description of the existing methods in all TRUS, MR and CT modalities, highlighting their key-points and features. Finally, a discussion on choosing the most appropriate segmentation strategy for a given imaging modality is provided. A quantitative comparison of the results as reported in literature is also presented.

Computerized detection and recognition of follicles in ovarian ultrasound images: a review

Observing changes in females' ovaries is essential in obstetrics and gynaecological imaging, e.g., genetic engineering and human reproduction. It is particularly important to monitor the dynamics of ovarian follicles' growth, as only fully mature and grown follicles, i.e., the dominant follicles have a potential to ovulate at the end of a follicular phase. Gynaecologists follow this process in two dimensions, but recently three-dimensional (3-D) ultrasound examinations are coming to the fore. This paper surveys the existing computer methods for detection, recognition, and analyses of follicles in two-dimensional (2-D) and 3-D ovarian ultrasound recordings. Our study focuses on the efficiency, validation, and assessment of proposed follicle processing algorithms. The most important processing steps were identified in order to compare their performances. Higher ranking solutions are suggested for the so-called best algorithm for 2-D and 3-D ultrasound recordings of ovarian follicles. Finally, some guidelines for future research in this field are discussed, in particular for 3-D ultrasound volumes.

A survey of GPU-based medical image computing techniques

Medical imaging currently plays a crucial role throughout the entire clinical applications from medical scientific research to diagnostics and treatment planning. However, medical imaging procedures are often computationally demanding due to the large three-dimensional (3D) medical datasets to process in practical clinical applications. With the rapidly enhancing performances of graphics processors, improved programming support, and excellent price-to-performance ratio, the graphics processing unit (GPU) has emerged as a competitive parallel computing platform for computationally expensive and demanding tasks in a wide range of medical image applications. The major purpose of this survey is to provide a comprehensive reference source for the starters or researchers involved in GPU-based medical image processing. Within this survey, the continuous advancement of GPU computing is reviewed and the existing traditional applications in three areas of medical image processing, namely, segmentation, registration and visualization, are surveyed. The potential advantages and associated challenges of current GPU-based medical imaging are also discussed to inspire future applications in medicine.

Segmentation of ultrasonic breast tumors based on homogeneous patch

PURPOSE

Accurately segmenting breast tumors in ultrasound (US) images is a difficult problem due to their specular nature and appearance of sonographic tumors. The current paper presents a variant of the normalized cut (NCut) algorithm based on homogeneous patches (HP-NCut) for the segmentation of ultrasonic breast tumors.

METHODS

A novel boundary-detection function is defined by combining texture and intensity information to find the fuzzy boundaries in US images. Subsequently, based on the precalculated boundary map, an adaptive neighborhood according to image location referred to as a homogeneous patch (HP) is proposed. HPs are guaranteed to spread within the same tissue region; thus, the statistics of primary features within the HPs is more reliable in distinguishing the different tissues and benefits subsequent segmentation. Finally, the fuzzy distribution of textons within HPs is used as final image features, and the segmentation is obtained using the NCut framework.

RESULTS

The HP-NCut algorithm was evaluated on a large dataset of 100 breast US images (50 benign and 50 malignant). The mean Hausdorff distance measure, the mean minimum Euclidean distance measure and similarity measure achieved 7.1 pixels, 1.58 pixels, and 86.67%, respectively, for benign tumors while those achieved 10.57 pixels, 1.98 pixels, and 84.41%, respectively, for malignant tumors.

CONCLUSIONS

The HP-NCut algorithm provided the improvement in accuracy and robustness compared with state-of-the-art methods. A conclusion that the HP-NCut algorithm is suitable for ultrasonic tumor segmentation problems can be drawn.

Ultrasound confidence maps using random walks

Advances in ultrasound system development have led to a substantial improvement of image quality and to an increased use of ultrasound in clinical practice. Nevertheless, ultrasound attenuation and shadowing artifacts cannot be entirely avoided and continue to challenge medical image computing algorithms. We introduce a method for estimating a per-pixel confidence in the information depicted by ultrasound images, referred to as an ultrasound confidence map, which emphasizes uncertainty in attenuated and/or shadow regions. Our main novelty is the modeling of the confidence estimation problem within a random walks framework by taking into account ultrasound specific constraints. The solution to the random walks equilibrium problem is global and takes the entire image content into account. As a result, our method is applicable to a variety of ultrasound image acquisition setups. We demonstrate the applicability of our confidence maps for ultrasound shadow detection, 3D freehand ultrasound reconstruction, and multi-modal image registration.

Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized Rayleigh mixture model

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images.

Comparative study on the performance of textural image features for active contour segmentation

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

Algorithm versus physicians variability evaluation in the cardiac chambers extraction

Congenital heart diseases are present in eight of every 1000 newborns. The diagnosis of those pathologies usually depends on the available imaging methods. A correct diagnosis requires a detailed observation of the heart chambers, wall motions, valves function, and quantitative evaluation of the cavity volumes. For that goal numerous automatic algorithms have been proposed to segment the echocardiographic images. In this paper, the authors evaluate the performance of a level set algorithm based on the phase symmetry approach and on a new logarithmic-based stopping function to extract the heart cavity contours simultaneously, and in a fully automatic way. The extracted cardiac borders are then statistically compared with the ones manually sketched by four physicians on a set of 240 cavities. Nonparametric statistical tests are conducted on the data using several figures of merit, in order to study the inter- and intraobserver variabilities among the four physicians and the level set algorithm, concerning to the extracted contours. The results show there is a great concordance about all the used similarity indexes. A higher interobserver variability was found among the physicians than the variability obtained when the algorithm versus physician performance is compared. The statistical analysis suggests the proposed algorithm produces results similar to the ones provided by the physicians.

3D ultrasound image segmentation using wavelet support vector machines

PURPOSE

Transrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy.

METHODS

This segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method.

RESULTS

The results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% ± 2.3% and that the sensitivity is 87.7% ± 4.9%.

CONCLUSIONS

The proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate.

The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods

We present a new supervised learning model designed for the automatic segmentation of the left ventricle (LV) of the heart in ultrasound images. We address the following problems inherent to supervised learning models: 1) the need of a large set of training images; 2) robustness to imaging conditions not present in the training data; and 3) complex search process. The innovations of our approach reside in a formulation that decouples the rigid and nonrigid detections, deep learning methods that model the appearance of the LV, and efficient derivative-based search algorithms. The functionality of our approach is evaluated using a data set of diseased cases containing 400 annotated images (from 12 sequences) and another data set of normal cases comprising 80 annotated images (from two sequences), where both sets present long axis views of the LV. Using several error measures to compute the degree of similarity between the manual and automatic segmentations, we show that our method not only has high sensitivity and specificity but also presents variations with respect to a gold standard (computed from the manual annotations of two experts) within interuser variability on a subset of the diseased cases. We also compare the segmentations produced by our approach and by two state-of-the-art LV segmentation models on the data set of normal cases, and the results show that our approach produces segmentations that are comparable to these two approaches using only 20 training images and increasing the training set to 400 images causes our approach to be generally more accurate. Finally, we show that efficient search methods reduce up to tenfold the complexity of the method while still producing competitive segmentations. In the future, we plan to include a dynamical model to improve the performance of the algorithm, to use semisupervised learning methods to reduce even more the dependence on rich and large training sets, and to design a shape model less dependent on the training set.

A robust graph-based segmentation method for breast tumors in ultrasound images

OBJECTIVES

This paper introduces a new graph-based method for segmenting breast tumors in US images.

BACKGROUND AND MOTIVATION

Segmentation for breast tumors in ultrasound (US) images is crucial for computer-aided diagnosis system, but it has always been a difficult task due to the defects inherent in the US images, such as speckles and low contrast.

METHODS

The proposed segmentation algorithm constructed a graph using improved neighborhood models. In addition, taking advantages of local statistics, a new pair-wise region comparison predicate that was insensitive to noises was proposed to determine the mergence of any two of adjacent subregions.

RESULTS AND CONCLUSION

Experimental results have shown that the proposed method could improve the segmentation accuracy by 1.5-5.6% in comparison with three often used segmentation methods, and should be capable of segmenting breast tumors in US images.

Completely automated segmentation approach for breast ultrasound images using multiple-domain features

Lesion segmentation is a challenging task for computer aided diagnosis systems. In this article, we propose a novel and fully automated segmentation approach for breast ultrasound (BUS) images. The major contributions of this work are: an efficient region-of-interest (ROI) generation method is developed and new features to characterize lesion boundaries are proposed. After a ROI is located automatically, two newly proposed lesion features (phase in max-energy orientation and radial distance), combined with a traditional intensity-and-texture feature, are utilized to detect the lesion by a trained artificial neural network. The proposed features are tested on a database of 120 images and the experimental results prove their strong distinguishing ability. Compared with other breast ultrasound segmentation methods, the proposed method improves the TP rate from 84.9% to 92.8%, similarity rate from 79.0% to 83.1% and reduces the FP rate from 14.1% to 12.0%, using the same database. In addition, sensitivity analysis demonstrates the robustness of the proposed method.

Automatic left ventricle detection in echocardiographic images for deformable contour initialization

The accurate left ventricular boundary detection in echocardiographic images allow cardiologists to study and assess cardiomyopathy in patients. Due to the tedious and time consuming manner of manually tracing the borders, deformable models are generally used for left ventricle segmentations. However, most deformable models require a good initialization, which is usually outlined manually by the user. In this paper, we propose an automated left ventricle detection method for two-dimensional echocardiographic images that could serve as an initialization for deformable models. The proposed approach consists of pre-processing and post-processing stages, coupled with the watershed segmentation. The pre-processing stage enhances the overall contrast and reduces speckle noise, whereas the post-processing enhances the segmented region and avoids the papillary muscles. The performance of the proposed method is evaluated on real data. Experimental results show that it is suitable for automatic contour initialization since no prior assumptions nor human interventions are required. Besides, the computational time taken is also lower compared to an existing method.

Three-dimensional, extended field-of-view ultrasound method for estimating large strain mechanical properties of the cervix during pregnancy

Cervical shortening and cervical insufficiency contribute to a significant number of preterm births. However, the deformation mechanisms that control how the cervix changes its shape from long and closed to short and dilated are not clear. Investigation of the biomechanical problem is limited by (1) lack of thorough characterization of the three-dimensional anatomical changes associated with cervical deformation and (2) difficulty measuring cervical tissue properties in vivo. The objective of the present study was to explore the feasibility of using three-dimensional ultrasound and fundal pressure to obtain anatomically-accurate numerical models of large-strain cervical deformation during pregnancy and enable noninvasive assessment of cervical-tissue compliance. Healthy subjects (n = 6) and one subject with acute cervical insufficiency in the midtrimester were studied. Extended field-of-view ultrasound images were obtained of the entire uterus and cervix. These images aided construction of anatomically accurate numerical models. Cervical loading was achieved with fundal pressure, which was quantified with a vaginal pressure catheter. In one subject, the anatomical response to fundal pressure was matched by a model-based simulation of the deformation response, thereby deriving the corresponding cervical mechanical properties and showing the feasibility of noninvasive assessment of compliance. The results of this pilot study demonstrate the feasibility of a biomechanical modeling framework for estimating cervical mechanical properties in vivo. An improved understanding of cervical biomechanical function will clarify the pathophysiology of cervical shortening.

Effect of a novel segmentation algorithm on radiologists' diagnosis of breast masses using ultrasound imaging

We investigated the effect of using a novel segmentation algorithm on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses using ultrasound. Five-hundred ten conventional ultrasound images were processed by a novel segmentation algorithm. Five radiologists were invited to analyze the original and computerized images independently. Performances of radiologists with or without computer aid were evaluated by receiver operating characteristic (ROC) curve analysis. The masses became more obvious after being processed by the segmentation algorithm. Without using the algorithm, the areas under the ROC curve (Az) of the five radiologists ranged from 0.70∼0.84. Using the algorithm, the Az increased significantly (range, 0.79∼0.88; p < 0.001). The proposed segmentation algorithm could improve the radiologists' diagnosis performance by reducing the image speckles and extracting the mass margin characteristics.

A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images

Segmentation of stones from abdominal ultrasound images is a unique challenge to the researchers because these images have heavy speckle noise and attenuated artifacts. In the previous renal calculi segmentation method, the stones were segmented from the medical ultra sound kidney stone images using Adaptive Neuro Fuzzy Inference System (ANFIS). But, the method lacks in sensitivity and specificity measures. The segmentation method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new segmentation method is proposed in this paper. Here, new region indicators and new modified watershed transformation is utilized. The proposed method is comprised of four major processes, namely, preprocessing, determination of outer and inner region indictors, modified watershed segmentation with ANFIS performance. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of proposed segmentation method in segmenting the kidney stones and the achieved improvement in sensitivity and specificity measures. Furthermore, the performance of the proposed technique is evaluated by comparing with the other segmentation methods.

B-spline explicit active surfaces: an efficient framework for real-time 3-D region-based segmentation

A new formulation of active contours based on explicit functions has been recently suggested. This novel framework allows real-time 3-D segmentation since it reduces the dimensionality of the segmentation problem. In this paper, we propose a B-spline formulation of this approach, which further improves the computational efficiency of the algorithm. We also show that this framework allows evolving the active contour using local region-based terms, thereby overcoming the limitations of the original method while preserving computational speed. The feasibility of real-time 3-D segmentation is demonstrated using simulated and medical data such as liver computer tomography and cardiac ultrasound images.

A dynamical appearance model based on multiscale sparse representation: segmentation of the left ventricle from 4D echocardiography

The spatio-temporal coherence in data plays an important role in echocardiographic segmentation. While learning offline dynamical priors from databases has received considerable attention, these priors may not be suitable for post-infarct patients and children with congenital heart disease. This paper presents a dynamical appearance model (DAM) driven by individual inherent data coherence. It employs multi-scale sparse representation of local appearance, learns online multiscale appearance dictionaries as the image sequence is segmented sequentially, and integrates a spectrum of complementary multiscale appearance information including intensity, multiscale local appearance, and dynamical shape predictions. It overcomes the limitations of database-driven statistical models and applies to a broader range of subjects. Results on 26 4D canine echocardiographic images acquired from both healthy and post-infarct subjects show that our method significantly improves segmentation accuracy and robustness compared to a conventional intensity model and our previous single-scale sparse representation method.

Active ellipse model and automatic chamber detection in apical views of echocardiography images

In this article, an automatic method for detection of all chambers in apical two- and four-chamber views is proposed. The method is based on four evolving ellipses with their sizes and alignments (centre point) gradually changing through iterations until they reach to the point that approximates the chamber boundaries. The interaction between the internal, external and inter-elliptic forces controls the simultaneous evolution of ellipses. Since no prior assumption of the approximate location is required with our approach, the specialists are not required to locate the centre points of chambers in apical images, making the overall segmentation fully automated. Moreover, the resultant ellipse inside a chamber could be used as the initial contour in segmentation techniques such as active contour models, where the initial contour has a significant role for higher accuracy and faster convergence. The simplicity of equations developed in our approach make for a computationally faster algorithm, compared with former approaches that utilize morphologic operators. Our evolving ellipse does not go beyond the gaps, a problem that normally exists within boundaries in echo images, making our overall segmentation process more robust against the gaps. To evaluate the proposed method, a subset of 80 images is selected and three observers are requested to manually draw best ellipses inside the images and compare them with our results. The obtained dice coefficient results (87.62 ± 4.53% for observer-1, 83.18 ± 6.20% for observer-2, 86.02 ± 5.16% for observer-3) indicate that the proposed method has a useful performance.

BREAST ULTRASOUND IMAGE SEGMENTATION BASED ON PARTICLE SWARM OPTIMIZATION AND THE CHARACTERISTICS OF BREAST TISSUE

Breast cancer occurs in over 8% of women during their lifetime, and is the leading cause of death among women. Sonography is superior to mammography because it has the ability to detect focal abnormalities in the dense breasts and has no side-effect. In this paper, we propose a novel automatic segmentation algorithm based on the characteristics of breast tissue and eliminating particle swarm optimization (EPSO) clustering analysis. The characteristics of mammary gland in breast ultrasound (BUS) images are analyzed and utilized, and a method based on step-down threshold technique is employed to locate the mammary gland area. The EPSO clustering algorithm utilizes the idea of "survival of the superior and weeding out the inferior". The experimental results demonstrate that the proposed approach can segment BUS image with high accuracy and low computational time. The EPSO clustering method reduces the computational time by 32.75% compared with the standard PSO clustering algorithm. The proposed approach would find wide applications in automatic lesion classification and computer aided diagnosis (CAD) systems of breast cancer.

Contour extraction of Drosophila embryos

Contour extraction of Drosophila (fruit fly) embryos is an important step to build a computational system for matching expression pattern of embryonic images to assist the discovery of the nature of genes. Automatic contour extraction of embryos is challenging due to severe image variations, including 1) the size, orientation, shape, and appearance of an embryo of interest; 2) the neighboring context of an embryo of interest (such as nontouching and touching neighboring embryos); and 3) illumination circumstance. In this paper, we propose an automatic framework for contour extraction of the embryo of interest in an embryonic image. The proposed framework contains three components. Its first component applies a mixture model of quadratic curves, with statistical features, to initialize the contour of the embryo of interest. An efficient method based on imbalanced image points is proposed to compute model parameters. The second component applies active contour model to refine embryo contours. The third component applies eigen-shape modeling to smooth jaggy contours caused by blurred embryo boundaries. We test the proposed framework on a data set of 8,000 embryonic images, and achieve promising accuracy (88 percent), that is, substantially higher than the-state-of-the-art results.

Detection of the whole myocardium in 2D-echocardiography for multiple orientations using a geometrically constrained level-set

The segmentation of the myocardium in echocardiographic images is an important task for the diagnosis of heart disease. This task is difficult due to the inherent problems of echographic images (i.e. low contrast, speckle noise, signal dropout, presence of shadows). In this article, we propose a method to segment the whole myocardium (endocardial and epicardial contours) in 2D echographic images. This is achieved using a level-set model constrained by a new shape formulation that allows to model both contours. The novelty of this work also lays in the fact that our framework allows to segment the whole myocardium for the four main views used in clinical routine. The method is validated on a dataset of clinical images and compared with expert segmentation.

Automated regional analysis of B-mode ultrasound images of skeletal muscle movement

To understand the functional significance of skeletal muscle anatomy, a method of quantifying local shape changes in different tissue structures during dynamic tasks is required. Taking advantage of the good spatial and temporal resolution of B-mode ultrasound imaging, we describe a method of automatically segmenting images into fascicle and aponeurosis regions and tracking movement of features, independently, in localized portions of each tissue. Ultrasound images (25 Hz) of the medial gastrocnemius muscle were collected from eight participants during ankle joint rotation (2° and 20°), isometric contractions (1, 5, and 50 Nm), and deep knee bends. A Kanade-Lucas-Tomasi feature tracker was used to identify and track any distinctive and persistent features within the image sequences. A velocity field representation of local movement was then found and subdivided between fascicle and aponeurosis regions using segmentations from a multiresolution active shape model (ASM). Movement in each region was quantified by interpolating the effect of the fields on a set of probes. ASM segmentation results were compared with hand-labeled data, while aponeurosis and fascicle movement were compared with results from a previously documented cross-correlation approach. ASM provided good image segmentations (<1 mm average error), with fully automatic initialization possible in sequences from seven participants. Feature tracking provided similar length change results to the cross-correlation approach for small movements, while outperforming it in larger movements. The proposed method provides the potential to distinguish between active and passive changes in muscle shape and model strain distributions during different movements/conditions and quantify nonhomogeneous strain along aponeuroses.

Segmentation of left ventricle in short-axis echocardiographic sequences by weighted radial edge filtering and adaptive recovery of dropout regions

In this paper, we present a weighted radial edge filtering algorithm with adaptive recovery of dropout regions for the semi-automatic delineation of endocardial contours in short-axis echocardiographic image sequences. The proposed algorithm requires minimal user intervention at the end diastolic frame of the image sequence for specifying the candidate points of the contour. The region of interest is identified by fitting an ellipse in the region defined by the specified points. Subsequently, the ellipse centre is used for originating the radial lines for filtering. A weighted radial edge filter is employed for the detection of edge points. The outliers are corrected by global as well as local statistics. Dropout regions are recovered by incorporating the important temporal information from the previous frame by means of recursive least squares adaptive filter. This ensures fairly accurate segmentation of the cardiac structures for further determination of the functional cardiac parameters. The proposed algorithm was applied to 10 data-sets over a full cardiac cycle and the results were validated by comparing computer-generated boundaries to those manually outlined by two experts using Hausdorff distance (HD) measure, radial mean square error (rmse) and contour similarity index. The rmse was 1.83 mm with a HD of 5.12 ± 1.21 mm. We have also compared our results with two existing approaches, level set and optical flow. The results indicate an improvement when compared with ground truth due to incorporation of temporal clues. The weighted radial edge filtering algorithm in conjunction with adaptive dropout recovery offers semi-automatic segmentation of heart chambers in 2D echocardiography sequences for accurate assessment of global left ventricular function to guide therapy and staging of the cardiovascular diseases.

Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor

This paper presents an algorithm for segmenting left ventricular endocardial boundaries from RF ultrasound. Our method incorporates a computationally efficient linear predictor that exploits short-term spatio-temporal coherence in the RF data. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model that relates neighboring frames in the image sequence. Segmentation using the RF data overcomes challenges due to image inhomogeneities often amplified in B-mode segmentation and provides geometric constraints for RF phase-based speckle tracking. The incorporation of multiple frames in the conditional model significantly increases the robustness and accuracy of the algorithm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from six canine studies.

Correlation based 3-D segmentation of the left ventricle in pediatric echocardiographic images using radio-frequency data

Clinical diagnosis of heart disease might be substantially supported by automated segmentation of the endocardial surface in three-dimensional (3-D) echographic images. Because of the poor echogenicity contrast between blood and myocardial tissue in some regions and the inherent speckle noise, automated analysis of these images is challenging. A priori knowledge on the shape of the heart cannot always be relied on, e.g., in children with congenital heart disease, segmentation should be based on the echo features solely. The objective of this study was to investigate the merit of using temporal cross-correlation of radio-frequency (RF) data for automated segmentation of 3-D echocardiographic images. Maximum temporal cross-correlation (MCC) values were determined locally from the RF-data using an iterative 3-D technique. MCC values as well as a combination of MCC values and adaptive filtered, demodulated RF-data were used as an additional, external force in a deformable model approach to segment the endocardial surface and were tested against manually segmented surfaces. Results on 3-D full volume images (Philips, iE33) of 10 healthy children demonstrate that MCC values derived from the RF signal yield a useful parameter to distinguish between blood and myocardium in regions with low echogenicity contrast and incorporation of MCC improves the segmentation results significantly. Further investigation of the MCC over the whole cardiac cycle is required to exploit the full benefit of it for automated segmentation.

Fisher-Tippett region-merging approach to transrectal ultrasound prostate lesion segmentation

In this paper, a computerized approach to segmenting prostate lesions in transrectal ultrasound (TRUS) images is presented. The segmentation of prostate lesions from TRUS images is very challenging due to issues, such as poor contrast, low SNRs, and irregular shape variations. To address these issues, a novel approach is employed to segment the lesions from the surrounding prostate, where region merging is performed via a region-merging likelihood function based on regional statistics, as well as Fisher-Tippett statistics. Experimental results using TRUS prostate images demonstrate that the proposed Fisher-Tippett region-merging approach achieves more accurate segmentation of prostate lesions when compared to other segmentation methods.

Ultrasonic image analysis and image-guided interventions

The fields of medical image analysis and computer-aided interventions deal with reducing the large volume of digital images (X-ray, computed tomography, magnetic resonance imaging (MRI), positron emission tomography and ultrasound (US)) to more meaningful clinical information using software algorithms. US is a core imaging modality employed in these areas, both in its own right and used in conjunction with the other imaging modalities. It is receiving increased interest owing to the recent introduction of three-dimensional US, significant improvements in US image quality, and better understanding of how to design algorithms which exploit the unique strengths and properties of this real-time imaging modality. This article reviews the current state of art in US image analysis and its application in image-guided interventions. The article concludes by giving a perspective from clinical cardiology which is one of the most advanced areas of clinical application of US image analysis and describing some probable future trends in this important area of ultrasonic imaging research.

Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features

Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.

Biplane US-Guided Real-Time Volumetric Target Pose Estimation Method for Theragnostic HIFU System

This paper presents a real-time pose estimation method as a part of robotic HIFU treatment system for moving volumetric targets. For the acquired biplane US images, current pose of the preoperative model is calculated by iterative segmentation and registration. Seed contours for the segmentation in each iteration is provided by previously registered preoperative 3-D model. The segmented boundary points then update the pose of 3-D model. The boundary outlier-removal makes the algorithm robust against partially noisy boundaries as well as the spatial boundary points accelerates the algorithm to be calculated in real-time. By the phantom experiments, registration accuracy for a biplane US image data was evaluated, and the processing time was also investigated.

The ultrasound brain helmet: new transducers and volume registration for in vivo simultaneous multi-transducer 3-D transcranial imaging

Because stroke remains an important and time-sensitive health concern in developed nations, we present a system capable of fusing 3-D transcranial ultrasound volumes acquired from two sides of the head. This system uses custom sparse array transducers built on flexible multilayer circuits that can be positioned for simultaneous imaging through both temporal acoustic windows, allowing for potential registration of multiple real-time 3-D scans of cerebral vasculature. We examine hardware considerations for new matrix arrays-transducer design and interconnects-in this application. Specifically, it is proposed that SNR may be increased by reducing the length of probe cables. This claim is evaluated as part of the presented system through simulation, experimental data, and in vivo imaging. Ultimately, gains in SNR of 7 dB are realized by replacing a standard probe cable with a much shorter flex interconnect; higher gains may be possible using ribbon-based probe cables. In vivo images are presented, showing cerebral arteries with and without the use of microbubble contrast agent; they have been registered and fused using a simple algorithm which maximizes normalized cross-correlation.

Thyroid nodule classification using ultrasound elastography via linear discriminant analysis

The non-surgical diagnosis of thyroid nodules is currently made via a fine needle aspiration (FNA) biopsy. It is estimated that somewhere between 250,000 and 300,000 thyroid FNA biopsies are performed in the United States annually. However, a large percentage (approximately 70%) of these biopsies turn out to be benign. Since the aggressive FNA management of thyroid nodules is costly, quantitative risk assessment and stratification of a nodule's malignancy is of value in triage and more appropriate healthcare resources utilization. In this paper, we introduce a new method for classifying the thyroid nodules based on the ultrasound (US) elastography features. Unlike approaches to assess the stiffness of a thyroid nodule by visually inspecting the pseudo-color pattern in the strain image, we use a classification algorithm to stratify the nodule by using the power spectrum of strain rate waveform extracted from the US elastography image sequence. Pulsation from the carotid artery was used to compress the thyroid nodules. Ultrasound data previously acquired from 98 thyroid nodules were used in this retrospective study to evaluate our classification algorithm. A classifier was developed based on the linear discriminant analysis (LDA) and used to differentiate the thyroid nodules into two types: (I) no FNA (observation-only) and (II) FNA. Using our method, 62 nodules were classified as type I, all of which were benign, while 36 nodules were classified as Type-II, 16 malignant and 20 benign, resulting in a sensitivity of 100% and specificity of 75.6% in detecting malignant thyroid nodules. This indicates that our triage method based on US elastography has the potential to substantially reduce the number of FNA biopsies (63.3%) by detecting benign nodules and managing them via follow-up observations rather than an FNA biopsy.

Visual servoing for a US-guided therapeutic HIFU system by coagulated lesion tracking: a phantom study

BACKGROUND

Applying ultrasound (US)-guided high-intensity focused ultrasound (HIFU) therapy for kidney tumours is currently very difficult, due to the unclearly observed tumour area and renal motion induced by human respiration. In this research, we propose new methods by which to track the indistinct tumour area and to compensate the respiratory tumour motion for US-guided HIFU treatment.

METHODS

For tracking indistinct tumour areas, we detect the US speckle change created by HIFU irradiation. In other words, HIFU thermal ablation can coagulate tissue in the tumour area and an intraoperatively created coagulated lesion (CL) is used as a spatial landmark for US visual tracking. Specifically, the condensation algorithm was applied to robust and real-time CL speckle pattern tracking in the sequence of US images. Moreover, biplanar US imaging was used to locate the three-dimensional position of the CL, and a three-actuator system drives the end-effector to compensate for the motion. Finally, we tested the proposed method by using a newly devised phantom model that enables both visual tracking and a thermal response by HIFU irradiation.

RESULTS

In the experiment, after generation of the CL in the phantom kidney, the end-effector successfully synchronized with the phantom motion, which was modelled by the captured motion data for the human kidney. The accuracy of the motion compensation was evaluated by the error between the end-effector and the respiratory motion, the RMS error of which was approximately 2 mm.

CONCLUSION

This research shows that a HIFU-induced CL provides a very good landmark for target motion tracking. By using the CL tracking method, target motion compensation can be realized in the US-guided robotic HIFU system.

Segmenting echocardiography images using B-Spline snake and active ellipse model

In this paper, a fully automated method for segmenting Left Ventricle (LV) in echocardiography images is proposed. A new method named active ellipse model is developed to automatically find the best ellipse inside the LV chamber without intervention of any specialist. A modified B-Spline Snake algorithm is used to segment the LV chamber in which the initial contour is formed by the predefined ellipse. As a result of using active ellipse model, the segmentation is extricated from dealing with gaps within myocardium boundary which are highly problematic in echocardiography image segmentation. Based on the results obtained from different studies, the proposed method is faster and more accurate than previous approaches. Our method is evaluated on 20 sets of echocardiography images of patients; and acquired results (92.30 ± 4.45% dice's coefficient) indicate the proposed method has remarkable performance.

Left ventricular border tracking using cardiac motion models and optical flow

The use of automated methods is becoming increasingly important for assessing cardiac function quantitatively and objectively. In this study, we propose a method for tracking three-dimensional (3-D) left ventricular contours. The method consists of a local optical flow tracker and a global tracker, which uses a statistical model of cardiac motion in an optical-flow formulation. We propose a combination of local and global trackers using gradient-based weights. The algorithm was tested on 35 echocardiographic sequences, with good results (surface error: 1.35 ± 0.46 mm, absolute volume error: 5.4 ± 4.8 mL). This demonstrates the method's potential in automated tracking in clinical quality echocardiograms, facilitating the quantitative and objective assessment of cardiac function.

Segmentation of plaques in sequences of ultrasonic B-mode images of carotid arteries based on motion estimation and a Bayesian model

The goal of this work is to perform a segmentation of atherosclerotic plaques in view of evaluating their burden and to provide boundaries for computing properties such as the plaque deformation and elasticity distribution (elastogram and modulogram). The echogenicity of a region of interest comprising the plaque, the vessel lumen, and the adventitia of the artery wall in an ultrasonic B-mode image was modeled by mixtures of three Nakagami distributions, which yielded the likelihood of a Bayesian segmentation model. The main contribution of this paper is the estimation of the motion field and its integration into the prior of the Bayesian model that included a local geometrical smoothness constraint, as well as an original spatiotemporal cohesion constraint. The Maximum A Posteriori (MAP) of the proposed model was computed with a variant of the Exploration/Selection (ES) algorithm. The starting point is a manual segmentation of the first frame. The proposed method was quantitatively compared with manual segmentations of all frames by an expert technician. Various measures were used for this evaluation, including the mean point-to-point distance and the Hausdorff distance. Results were evaluated on 94 sequences of 33 patients (for a total of 8988 images). We report a mean point-to- point distance of 0.24 ± 0.08 mm and a Hausdorff distance of 1.24 ± 0.40 mm. Our tests showed that the algorithm was not sensitive to the degree of stenosis or calcification.

Automatic 3D Segmentation of Ultrasound Images Using Atlas Registration and Statistical Texture Prior

We are developing a molecular image-directed, 3D ultrasound-guided, targeted biopsy system for improved detection of prostate cancer. In this paper, we propose an automatic 3D segmentation method for transrectal ultrasound (TRUS) images, which is based on multi-atlas registration and statistical texture prior. The atlas database includes registered TRUS images from previous patients and their segmented prostate surfaces. Three orthogonal Gabor filter banks are used to extract texture features from each image in the database. Patient-specific Gabor features from the atlas database are used to train kernel support vector machines (KSVMs) and then to segment the prostate image from a new patient. The segmentation method was tested in TRUS data from 5 patients. The average surface distance between our method and manual segmentation is 1.61 ± 0.35 mm, indicating that the atlas-based automatic segmentation method works well and could be used for 3D ultrasound-guided prostate biopsy.