Ultrasound image segmentation: a survey

Phase-based probabilistic active contour for nerve detection in ultrasound images for regional anesthesia

Ultrasound guided regional anesthesia (UGRA) is steadily growing in popularity, owing to advances in ultrasound imaging technology and the advantages that this technique presents for safety and efficiency. The aim of this work is to assist anaesthetists during the UGRA procedure by automatically detecting the nerve blocks in the ultrasound images. The main disadvantage of ultrasound images is the poor quality of the images, which are also affected by the speckle noise. Moreover, the nerve structure is not salient amid the other tissues, which makes its detection a challenging problem. In this paper we propose a new method to tackle the problem of nerve zone detection in ultrasound images. The method consists in a combination of three approaches: probabilistic, edge phase information and active contours. The gradient vector flow (GVF) is adopted as an edge-based active contour. The phase analysis of the monogenic signal is used to provide reliable edges for the GVF. Then, a learned probabilistic model reduces the false positives and increases the likelihood energy term of the target region. It yields a new external force field that attracts the active contour toward the desired region of interest. The proposed scheme has been applied to sciatic nerve regions. The qualitative and quantitative evaluations show a high accuracy and a significant improvement in performance.

Automatic detection and measurement of structures in fetal head ultrasound volumes using sequential estimation and Integrated Detection Network (IDN)

Routine ultrasound exam in the second and third trimesters of pregnancy involves manually measuring fetal head and brain structures in 2-D scans. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. This paper proposes an automatic fetal head and brain (AFHB) system for automatically measuring anatomical structures from 3-D ultrasound volumes. The system searches the 3-D volume in a hierarchy of resolutions and by focusing on regions that are likely to be the measured anatomy. The output is a standardized visualization of the plane with correct orientation and centering as well as the biometric measurement of the anatomy. The system is based on a novel framework for detecting multiple structures in 3-D volumes. Since a joint model is difficult to obtain in most practical situations, the structures are detected in a sequence, one-by-one. The detection relies on Sequential Estimation techniques, frequently applied to visual tracking. The interdependence of structure poses and strong prior information embedded in our domain yields faster and more accurate results than detecting the objects individually. The posterior distribution of the structure pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple structures and hierarchical levels. The probabilistic model helps solve many challenges present in the ultrasound images of the fetus such as speckle noise, signal drop-out, shadows caused by bones, and appearance variations caused by the differences in the fetus gestational age. This is possible by discriminative learning on an extensive database of scans comprising more than two thousand volumes and more than thirteen thousand annotations. The average difference between ground truth and automatic measurements is below 2 mm with a running time of 6.9 s (GPU) or 14.7 s (CPU). The accuracy of the AFHB system is within inter-user variability and the running time is fast, which meets the requirements for clinical use.

A Hybrid Technique of Noise Reduction with Periductal Fibrosis Ultrasound Images for Periductal Fibrosis Detection System of Cholangiocarcinoma Surveillance

The Cholangiocarcinoma (CCA) is a serious public health problem. The Periductal fibrosis (PDF) ultrasound images are applied for CCA surveillance because it is no side effect of radiation with patients, easy to portability and low cost. In contrast, the common problem of ultrasound images are speckle noise in which decreases the PDF detection performance. In this paper proposes a hybrid noise reduction method in the PDF detection system. The proposed noise reduction method by applying the Median filter and Fast Fourier transform based on PDF ultrasound images. The experimental results give the best performance for PDF detection system. A success rate of proposed method achieved at 70.89%.

Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.

Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery

Ultrasound imaging of the common carotid artery (CCA) is a non-invasive tool used in medicine to assess the severity of atherosclerosis and monitor its progression through time. It is also used in border detection and texture characterization of the atherosclerotic carotid plaque in the CCA, the identification and measurement of the intima-media thickness (IMT) and the lumen diameter that all are very important in the assessment of cardiovascular disease (CVD). Visual perception, however, is hindered by speckle, a multiplicative noise, that degrades the quality of ultrasound B-mode imaging. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image segmentation of the IMT and the atherosclerotic carotid plaque in ultrasound images. In order to facilitate this preprocessing step, we have developed in MATLAB(®) a unified toolbox that integrates image despeckle filtering (IDF), texture analysis and image quality evaluation techniques to automate the pre-processing and complement the disease evaluation in ultrasound CCA images. The proposed software, is based on a graphical user interface (GUI) and incorporates image normalization, 10 different despeckle filtering techniques (DsFlsmv, DsFwiener, DsFlsminsc, DsFkuwahara, DsFgf, DsFmedian, DsFhmedian, DsFad, DsFnldif, DsFsrad), image intensity normalization, 65 texture features, 15 quantitative image quality metrics and objective image quality evaluation. The software is publicly available in an executable form, which can be downloaded from http://www.cs.ucy.ac.cy/medinfo/. It was validated on 100 ultrasound images of the CCA, by comparing its results with quantitative visual analysis performed by a medical expert. It was observed that the despeckle filters DsFlsmv, and DsFhmedian improved image quality perception (based on the expert's assessment and the image texture and quality metrics). It is anticipated that the system could help the physician in the assessment of cardiovascular image analysis.

Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images

We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a "global" 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2%±2.0% for 3-D TRUS images and 88.5%±3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers.

A New CT Prostate Segmentation for CT-Based HDR Brachytherapy

High-dose-rate (HDR) brachytherapy has become a popular treatment modality for localized prostate cancer. Prostate HDR treatment involves placing 10 to 20 catheters (needles) into the prostate gland, and then delivering radiation dose to the cancerous regions through these catheters. These catheters are often inserted with transrectal ultrasound (TRUS) guidance and the HDR treatment plan is based on the CT images. The main challenge for CT-based HDR planning is to accurately segment prostate volume in CT images due to the poor soft tissue contrast and additional artifacts introduced by the catheters. To overcome these limitations, we propose a novel approach to segment the prostate in CT images through TRUS-CT deformable registration based on the catheter locations. In this approach, the HDR catheters are reconstructed from the intra-operative TRUS and planning CT images, and then used as landmarks for the TRUS-CT image registration. The prostate contour generated from the TRUS images captured during the ultrasound-guided HDR procedure was used to segment the prostate on the CT images through deformable registration. We conducted two studies. A prostate-phantom study demonstrated a submillimeter accuracy of our method. A pilot study of 5 prostate-cancer patients was conducted to further test its clinical feasibility. All patients had 3 gold markers implanted in the prostate that were used to evaluate the registration accuracy, as well as previous diagnostic MR images that were used as the gold standard to assess the prostate segmentation. For the 5 patients, the mean gold-marker displacement was 1.2 mm; the prostate volume difference between our approach and the MRI was 7.2%, and the Dice volume overlap was over 91%. Our proposed method could improve prostate delineation, enable accurate dose planning and delivery, and potentially enhance prostate HDR treatment outcome.

Correlation-based discrimination between cardiac tissue and blood for segmentation of the left ventricle in 3-D echocardiographic images

For automated segmentation of 3-D echocardiographic images, incorporation of temporal information may be helpful. In this study, optimal settings for calculation of temporal cross-correlations between subsequent time frames were determined, to obtain the maximum cross-correlation (MCC) values that provided the best contrast between blood and cardiac tissue over the entire cardiac cycle. Both contrast and boundary gradient quality measures were assessed to optimize MCC values with respect to signal choice (radiofrequency or envelope data) and axial window size. Optimal MCC values were incorporated into a deformable model to automatically segment the left ventricular cavity. MCC values were tested against, and combined with, filtered, demodulated radiofrequency data. Results reveal that using envelope data in combination with a relatively small axial window (0.7-1.25 mm) at fine scale results in optimal contrast and boundary gradient between the two tissues over the entire cardiac cycle. Preliminary segmentation results indicate that incorporation of MCC values has additional value for automated segmentation of the left ventricle.

Whole myocardium tracking in 2D-echocardiography in multiple orientations using a motion constrained level-set

The segmentation and tracking of the myocardium in echocardiographic sequences 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 extend a level-set method recently proposed in Dietenbeck et al. (2012) in order to track the whole myocardium in echocardiographic sequences. To this end, we enforce temporal coherence by adding a new motion prior energy to the existing framework. This motion prior term is expressed as new constraint that enforces the conservation of the levels of the implicit function along the image sequence. Moreover, the robustness of the proposed method is improved by adjusting the associated hyperparameters in a spatially adaptive way, using the available strong a priori about the echocardiographic regions to be segmented. The accuracy and robustness of the proposed method is evaluated by comparing the obtained segmentation with experts references and to another state-of-the-art method on a dataset of 15 sequences (≃ 900 images) acquired in three echocardiographic views. We show that the algorithm provides results that are consistent with the inter-observer variability and outperforms the state-of-the-art method. We also carry out a complete study on the influence of the parameters settings. The obtained results demonstrate the stability of our method according to those values.

Rheological assessment of a polymeric spherical structure using a three-dimensional shear wave scattering model in dynamic spectroscopy elastography

With the purpose of assessing localized rheological behavior of pathological tissues using ultrasound dynamic elastography, an analytical shear wave scattering model was used in an inverse problem framework. The proposed method was adopted to estimate the complex shear modulus of viscoelastic spheres from 200 to 450 Hz. The inverse problem was formulated and solved in the frequency domain, allowing assessment of the complex viscoelastic shear modulus at discrete frequencies. A representative rheological model of the spherical obstacle was determined by comparing storage and loss modulus behaviors with Kelvin-Voigt, Maxwell, Zener, and Jeffrey models. The proposed inversion method was validated by using an external vibrating source and acoustic radiation force. The estimation of viscoelastic properties of three-dimensional spheres made softer or harder than surrounding tissues did not require a priori rheological assumptions. The proposed method is intended to be applied in the context of breast cancer imaging.

Contour tracking in echocardiographic sequences via sparse representation and dictionary learning

This paper presents a dynamical appearance model based on sparse representation and dictionary learning for tracking both endocardial and epicardial contours of the left ventricle in echocardiographic sequences. Instead of learning offline spatiotemporal priors from databases, we exploit the inherent spatiotemporal coherence of individual data to constraint cardiac contour estimation. The contour tracker is initialized with a manual tracing of the first frame. It employs multiscale sparse representation of local image appearance and learns online multiscale appearance dictionaries in a boosting framework as the image sequence is segmented frame-by-frame sequentially. The weights of multiscale appearance dictionaries are optimized automatically. Our region-based level set segmentation integrates a spectrum of complementary multilevel information including intensity, multiscale local appearance, and dynamical shape prediction. The approach is validated on twenty-six 4D canine echocardiographic images acquired from both healthy and post-infarct canines. The segmentation results agree well with expert manual tracings. The ejection fraction estimates also show good agreement with manual results. Advantages of our approach are demonstrated by comparisons with a conventional pure intensity model, a registration-based contour tracker, and a state-of-the-art database-dependent offline dynamical shape model. We also demonstrate the feasibility of clinical application by applying the method to four 4D human data sets.

Region and boundary feature estimation on ultrasound images using moment invariants

In ultrasound images, tissues are characterized by their speckle texture. Moment-based techniques have proven their ability to capture texture features. However, in ultrasound images, the speckle size increases with the distance from the probe and in some cases the speckle has a concentric texture arrangement. We propose to use moment invariants with respect to image scale and rotation to capture the texture in such cases. Results on synthetic data show that moment invariants are able to characterize the texture but also that some moment orders are sensitive to regions and, moreover, some are sensitive to the boundaries between two different textures. This behavior seems to be very interesting to be used within some segmentation scheme dealing with a combination of regional and boundary information. In this paper we will try to prove the usability of this complementary information in a min-cut/max-flow graph cut scheme.

Bubble stream reveals functionality of the right-to-left shunt: detection of a potential source for air embolism

The existence of a right-to-left shunt may increase the likelihood of micro-embolism by allowing a flux of bubbles under hyperbaric conditions. The aim of the study was to measure the relationship between these shunts and bubbles in 10 consecutive subjects using trans-thoracic and trans-esophageal echocardiography. In video frames, all cardiac chambers were segmented and bubbles were analyzed by our proposed method and two other methods. The relationship with bubbles and shunts was divided into three classes: no bubbles, 1-20 bubbles, >20 bubbles and measured over 2160 frames. Our sensitivity was 100% and our specificity was between 90.1% and 96.4%. There were 4.32-23.78 bubbles/frame in the left atrium according to our method. After the automatic analysis, shunts were graded double-blinded by two cardiologists. Consequently, we noted that aperture size does not necessarily reflect how active the right-to-left shunt is. Instead, our proposed decay curves constitute a better tool for determining functionality.

Three-dimensional segmentation of three-dimensional ultrasound carotid atherosclerosis using sparse field level sets

PURPOSE

Three-dimensional ultrasound (3DUS) vessel wall volume (VWV) provides a 3D measurement of carotid artery wall remodeling and atherosclerotic plaque and is sensitive to temporal changes of carotid plaque burden. Unfortunately, although 3DUS VWV provides many advantages compared to measurements of arterial wall thickening or plaque alone, it is still not widely used in research or clinical practice because of the inordinate amount of time required to train observers and to generate 3DUS VWV measurements. In this regard, semiautomated methods for segmentation of the carotid media-adventitia boundary (MAB) and the lumen-intima boundary (LIB) would greatly improve the time to train observers and for them to generate 3DUS VWV measurements with high reproducibility.

METHODS

The authors describe a 3D algorithm based on a modified sparse field level set method for segmenting the MAB and LIB of the common carotid artery (CCA) from 3DUS images. To the authors' knowledge, the proposed algorithm is the first direct 3D segmentation method, which has been validated for segmenting both the carotid MAB and the LIB from 3DUS images for the purpose of computing VWV. Initialization of the algorithm requires the observer to choose anchor points on each boundary on a set of transverse slices with a user-specified interslice distance (ISD), in which larger ISD requires fewer user interactions than smaller ISD. To address the challenges of the MAB and LIB segmentations from 3DUS images, the authors integrated regional- and boundary-based image statistics, expert initializations, and anatomically motivated boundary separation into the segmentation. The MAB is segmented by incorporating local region-based image information, image gradients, and the anchor points provided by the observer. Moreover, a local smoothness term is utilized to maintain the smooth surface of the MAB. The LIB is segmented by constraining its evolution using the already segmented surface of the MAB, in addition to the global region-based information and the anchor points. The algorithm-generated surfaces were sliced and evaluated with respect to manual segmentations on a slice-by-slice basis using 21 3DUS images.

RESULTS

The authors used ISD of 1, 2, 3, 4, and 10 mm for algorithm initialization to generate segmentation results. The algorithm-generated accuracy and intraobserver variability results are comparable to the previous methods, but with fewer user interactions. For example, for the ISD of 3 mm, the algorithm yielded an average Dice coefficient of 94.4% ± 2.2% and 90.6% ± 5.0% for the MAB and LIB and the coefficient of variation of 6.8% for computing the VWV of the CCA, while requiring only 1.72 min (vs 8.3 min for manual segmentation) for a 3DUS image.

CONCLUSIONS

The proposed 3D semiautomated segmentation algorithm yielded high-accuracy and high-repeatability, while reducing the expert interaction required for initializing the algorithm than the previous 2D methods.

Endocardial surface delineation in 3-D transesophageal echocardiography

We describe and compare several methods for recovering endocardial walls from 3-D transesophageal echocardiography (3-D TEE), which can help with diagnostics or providing input into biomechanical models. We employ a segmentation method based on 3-D level sets that maximizes enclosed volume while minimizing surface area and uses a growth inhibition function that includes 3-D gradient magnitude (to locate the endocardial walls) and a thin tissue detector (for the mitral valve leaflets). We also study delineation using a graph cut method that performs automated seeding by leveraging a fast radial symmetry transform to determine a central axis along which the 3-D volume is warped into a cylindrical coordinate space. Finally, a random walker approach is also used for automated delineation. The methods are used to estimate clinically relevant cardiovascular volumetric parameters such as stroke volume and left ventricular ejection fraction. Experiments are performed on clinical data collected from patients undergoing cardiothoracic surgery. Performance evaluation includes comparisons of the automated delineations against expert-defined ground truth using a number of error metrics, as well as errors between automatically computed and expert-derived physiologic parameters.

Detection of Cardiac Quiescence from B-Mode Echocardiography Using a Correlation-Based Frame-to-Frame Deviation Measure

Two novel methods for detecting cardiac quiescent phases from B-mode echocardiography using a correlation-based frame-to-frame deviation measure were developed. Accurate knowledge of cardiac quiescence is crucial to the performance of many imaging modalities, including computed tomography coronary angiography (CTCA). Synchronous electrocardiography (ECG) and echocardiography data were obtained from 10 healthy human subjects (four male, six female, 23–45 years) and the interventricular septum (IVS) was observed using the apical four-chamber echocardiographic view. The velocity of the IVS was derived from active contour tracking and verified using tissue Doppler imaging echocardiography methods. In turn, the frame-to-frame deviation methods for identifying quiescence of the IVS were verified using active contour tracking. The timing of the diastolic quiescent phase was found to exhibit both inter- and intra-subject variability, suggesting that the current method of CTCA gating based on the ECG is suboptimal and that gating based on signals derived from cardiac motion are likely more accurate in predicting quiescence for cardiac imaging. Two robust and efficient methods for identifying cardiac quiescent phases from B-mode echocardiographic data were developed and verified. The methods presented in this paper will be used to develop new CTCA gating techniques and quantify the resulting potential improvement in CTCA image quality.

An efficient neural network based method for medical image segmentation

The aim of this research is to propose a new neural network based method for medical image segmentation. Firstly, a modified self-organizing map (SOM) network, named moving average SOM (MA-SOM), is utilized to segment medical images. After the initial segmentation stage, a merging process is designed to connect the objects of a joint cluster together. A two-dimensional (2D) discrete wavelet transform (DWT) is used to build the input feature space of the network. The experimental results show that MA-SOM is robust to noise and it determines the input image pattern properly. The segmentation results of breast ultrasound images (BUS) demonstrate that there is a significant correlation between the tumor region selected by a physician and the tumor region segmented by our proposed method. In addition, the proposed method segments X-ray computerized tomography (CT) and magnetic resonance (MR) head images much better than the incremental supervised neural network (ISNN) and SOM-based methods.

Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data

We present a new statistical pattern recognition approach for the problem of left ventricle endocardium tracking in ultrasound data. The problem is formulated as a sequential importance resampling algorithm such that the expected segmentation of the current time step is estimated based on the appearance, shape, and motion models that take into account all previous and current images and previous segmentation contours produced by the method. The new appearance and shape models decouple the affine and nonrigid segmentations of the left ventricle to reduce the running time complexity. The proposed motion model combines the systole and diastole motion patterns and an observation distribution built by a deep neural network. The functionality of our approach is evaluated using a dataset of diseased cases containing 16 sequences and another dataset of normal cases comprised of four sequences, where both sets present long axis views of the left ventricle. Using a training set comprised of diseased and healthy cases, we show that our approach produces more accurate results than current state-of-the-art endocardium tracking methods in two test sequences from healthy subjects. Using three test sequences containing different types of cardiopathies, we show that our method correlates well with interuser statistics produced by four cardiologists.

Automated skin segmentation in ultrasonic evaluation of skin toxicity in breast cancer radiotherapy

Skin toxicity is the most common side effect of breast cancer radiotherapy and impairs the quality of life of many breast cancer survivors. We, along with other researchers, have recently found quantitative ultrasound to be effective as a skin toxicity assessment tool. Although more reliable than standard clinical evaluations (visual observation and palpation), the current procedure for ultrasound-based skin toxicity measurements requires manual delineation of the skin layers (i.e., epidermis-dermis and dermis-hypodermis interfaces) on each ultrasound B-mode image. Manual skin segmentation is time consuming and subjective. Moreover, radiation-induced skin injury may decrease image contrast between the dermis and hypodermis, which increases the difficulty of delineation. Therefore, we have developed an automatic skin segmentation tool (ASST) based on the active contour model with two significant modifications: (i) The proposed algorithm introduces a novel dual-curve scheme for the double skin layer extraction, as opposed to the original single active contour method. (ii) The proposed algorithm is based on a geometric contour framework as opposed to the previous parametric algorithm. This ASST algorithm was tested on a breast cancer image database of 730 ultrasound breast images (73 ultrasound studies of 23 patients). We compared skin segmentation results obtained with the ASST with manual contours performed by two physicians. The average percentage differences in skin thickness between the ASST measurement and that of each physician were less than 5% (4.8 ± 17.8% and -3.8 ± 21.1%, respectively). In summary, we have developed an automatic skin segmentation method that ensures objective assessment of radiation-induced changes in skin thickness. Our ultrasound technology offers a unique opportunity to quantify tissue injury in a more meaningful and reproducible manner than the subjective assessments currently employed in the clinic.

A novel aortic valve segmentation from ultrasound image using continuous max-flow approach

Geometric features of aortic valve can be applied in diagnostic, modeling and image-guided cardiac intervention, however methods to accurately and effectively delineate aortic valve from ultrasound (US) image are not well addressed. This paper proposes a novel aortic valve segmentation algorithm for intra-operative 2D short-axis US image using probability estimation and continuous max-flow (CMF) approach. The algorithm first calculates composite probability estimation (CPE) and single probability estimation (SPE) over 5 prior images based on both intensity and distance to the corresponding centroid, then the energy function for the current input image is constructed, followed by a Graphic Processing Unit (GPU) accelerated CMF approach to achieve aortic valve contours in approximately real time. Quantitative evaluations over 270 images acquired from 3 subjects indicated the results of the algorithm had good correlation with the manual segmentation results (ground truth) by an expert. The Average Symmetric Contour Distance (ASCD), Dice Metric (DM), and Reliability were employed to evaluate our algorithm, and the evaluation results of these three metrics were 1.79±0.46 (in pixels), 0.96±0.01 and 0.84 (d=0.95) respectively, where the computational time was 39.23±5.02 ms per frame.

Aortic valve segmentation from ultrasound images based on shape constraint CV model

Image Guided Intervention for valvular heart disease is increasingly making progress in minimally invasive manner, where effective and accurate segmentation of aortic valve (AV) from echocardiography is fundamental to improve the intra operative location accuracy. This paper proposes a shape constraint Chan-Vese (CV) model for segmenting the AV from ultrasound (US) images. Considering the poor quality and speckle noise in AV US images, the problem of the overflow at the weak edge is solved by adding the shape constraint to the CV model. The predefined shape constructed from AV region is applied as an energy constraint to the energy function through a signed distance map, and the AV is detected from the US image by minimizing the energy function. A hundred AV segmentation results are analyzed in the experiment, where the evaluation parameters are 95.38 ± 2.7%, 1.4 ± 0.5 mm, 2.07 ± 1.3 mm in transthoracic AV and 97.21 ± 1.6%, 0.7 ± 0.15 mm, 1.04 ± 0.6 mm in transesophageal AV, which reveal that the shape constraint CV model can segment AV accurately, efficiently and robustly.

Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set

An automatic segmentation framework is proposed to segment the right ventricle (RV) in echocardiographic images. The method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform, a training model, and a localized region-based level set. First, the sparse matrix transform extracts main motion regions of the myocardium as eigen-images by analyzing the statistical information of the images. Second, an RV training model is registered to the eigen-images in order to locate the position of the RV. Third, the training model is adjusted and then serves as an optimized initialization for the segmentation of each image. Finally, based on the initializations, a localized, region-based level set algorithm is applied to segment both epicardial and endocardial boundaries in each echocardiograph. Three evaluation methods were used to validate the performance of the segmentation framework. The Dice coefficient measures the overall agreement between the manual and automatic segmentation. The absolute distance and the Hausdorff distance between the boundaries from manual and automatic segmentation were used to measure the accuracy of the segmentation. Ultrasound images of human subjects were used for validation. For the epicardial and endocardial boundaries, the Dice coefficients were 90.8 ± 1.7% and 87.3 ± 1.9%, the absolute distances were 2.0 ± 0.42 mm and 1.79 ± 0.45 mm, and the Hausdorff distances were 6.86 ± 1.71 mm and 7.02 ± 1.17 mm, respectively. The automatic segmentation method based on a sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

A new energy framework with distribution descriptors for image segmentation

Segmentation of the target object(s) from images that have multiple complicated regions, mixture intensity distributions or are corrupted by noise poses a challenge for the level set models. In addition, the conventional piecewise smooth level set models normally require prior knowledge about the number of image segments. To address these problems, we propose a novel segmentation energy function with two distribution descriptors to model the background and the target. The single background descriptor models the heterogeneous background with multiple regions. Then, the target descriptor takes into account the intensity distribution and incorporates local spatial constraint. Our descriptors, which have more complete distribution information, construct the unique energy function to differentiate the target from the background and are more tolerant of image noise. We compare our approach to three other level set models: 1) the Chan-Vese; 2) the multiphase level set; and 3) the geodesic level set. This comparison using 260 synthetic images with varying levels and types of image noise and medical images with more complicated backgrounds showed that our method outperforms these models for accuracy and immunity to noise. On an additional set of 300 synthetic images, our model is also less sensitive to the contour initialization as well as to different types and levels of noise.

Carotid artery segmentation in ultrasound images and measurement of intima-media thickness

Background. The segmentation of the common carotid artery (CCA) wall is imperative for the determination of the intima-media thickness (IMT) on B-mode ultrasound (US) images. The IMT is considered an important indicator in the evaluation of the risk for the development of atherosclerosis. In this paper, authors have discussed the relevance of measurements in clinical practices and the challenges that one has to face while approaching the segmentation of carotid artery on ultrasound images. The paper presents an overall review of commonly used methods for the CCA segmentation and IMT measurement along with the different performance metrics that have been proposed and used for performance validation. Summary and future directions are given in the conclusion.

Prostate tissue characterization/classification in 144 patient population using wavelet and higher order spectra features from transrectal ultrasound images

In this work, we have proposed an on-line computer-aided diagnostic system called "UroImage" that classifies a Transrectal Ultrasound (TRUS) image into cancerous or non-cancerous with the help of non-linear Higher Order Spectra (HOS) features and Discrete Wavelet Transform (DWT) coefficients. The UroImage system consists of an on-line system where five significant features (one DWT-based feature and four HOS-based features) are extracted from the test image. These on-line features are transformed by the classifier parameters obtained using the training dataset to determine the class. We trained and tested six classifiers. The dataset used for evaluation had 144 TRUS images which were split into training and testing sets. Three-fold and ten-fold cross-validation protocols were adopted for training and estimating the accuracy of the classifiers. The ground truth used for training was obtained using the biopsy results. Among the six classifiers, using 10-fold cross-validation technique, Support Vector Machine and Fuzzy Sugeno classifiers presented the best classification accuracy of 97.9% with equally high values for sensitivity, specificity and positive predictive value. Our proposed automated system, which achieved more than 95% values for all the performance measures, can be an adjunct tool to provide an initial diagnosis for the identification of patients with prostate cancer. The technique, however, is limited by the limitations of 2D ultrasound guided biopsy, and we intend to improve our technique by using 3D TRUS images in the future.

Ultrasound bio-microscopic image segmentation for evaluation of zebrafish cardiac function

Zebrafish can fully regenerate their myocardium after ventricular resection without evidence of scars. This extraordinary regenerative ability provides an excellent model system to study the activation of the regenerative potential for human heart tissue. In addition to the morphology, it is vital to understand the cardiac function of zebrafish. To characterize adult zebrafish cardiac function, an ultrasound biomicroscope (UBM) was customized for real-time imaging of the zebrafish heart (about 1 mm in diameter) at a resolution of around 37 μm. Moreover, we developed an image segmentation algorithm to track the cardiac boundary and measure the dynamic size of the zebrafish heart for further quantification of zebrafish cardiac function. The effectiveness and accuracy of the proposed segmentation algorithm were verified on a tissuemimicking phantom and in vivo zebrafish echocardiography. The quantitative evaluation demonstrated that the accuracy of the proposed algorithm is comparable to the manual delineation by experts.

Automatic Segmentation of Right Ventricle on Ultrasound Images Using Sparse Matrix Transform and Level Set

An automatic framework is proposed to segment right ventricle on ultrasound images. This method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform (SMT), a training model, and a localized region based level set. First, the sparse matrix transform extracts main motion regions of myocardium as eigenimages by analyzing statistical information of these images. Second, a training model of right ventricle is registered to the extracted eigenimages in order to automatically detect the main location of the right ventricle and the corresponding transform relationship between the training model and the SMT-extracted results in the series. Third, the training model is then adjusted as an adapted initialization for the segmentation of each image in the series. Finally, based on the adapted initializations, a localized region based level set algorithm is applied to segment both epicardial and endocardial boundaries of the right ventricle from the whole series. Experimental results from real subject data validated the performance of the proposed framework in segmenting right ventricle from echocardiography. The mean Dice scores for both epicardial and endocardial boundaries are 89.1%±2.3% and 83.6±7.3%, respectively. The automatic segmentation method based on sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle

In this paper, a novel lesion segmentation within breast ultrasound (BUS) image based on the cellular automata principle is proposed. Its energy transition function is formulated based on global image information difference and local image information difference using different energy transfer strategies. First, an energy decrease strategy is used for modeling the spatial relation information of pixels. For modeling global image information difference, a seed information comparison function is developed using an energy preserve strategy. Then, a texture information comparison function is proposed for considering local image difference in different regions, which is helpful for handling blurry boundaries. Moreover, two neighborhood systems (von Neumann and Moore neighborhood systems) are integrated as the evolution environment, and a similarity-based criterion is used for suppressing noise and reducing computation complexity. The proposed method was applied to 205 clinical BUS images for studying its characteristic and functionality, and several overlapping area error metrics and statistical evaluation methods are utilized for evaluating its performance. The experimental results demonstrate that the proposed method can handle BUS images with blurry boundaries and low contrast well and can segment breast lesions accurately and effectively.

Breast ultrasound image classification based on multiple-instance learning

Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p < 0.005).

Target detection in diagnostic ultrasound: Evaluation of a method based on the CLEAN algorithm

A technique is proposed for the detection of abnormalities (targets) in ultrasound images using little or no a priori information and requiring little operator intervention. The scheme is a combination of the CLEAN algorithm, originally proposed for radio astronomy, and constant false alarm rate (CFAR) processing, as developed for use in radar systems. The CLEAN algorithm identifies areas in the ultrasound image that stand out above a threshold in relation to the background; CFAR techniques allow for an adaptive, semi-automated, selection of the threshold. Neither appears to have been previously used for target detection in ultrasound images and never together in any context. As a first step towards assessing the potential of this method we used a widely used method of simulating B-mode images (Field II). We assumed the use of a 256 element linear array operating at 3.0MHz into a water-like medium containing a density of point scatterers sufficient to simulate a background of fully developed speckle. Spherical targets with diameters ranging from 0.25 to 6.0mm and contrasts ranging from 0 to 12dB relative to the background were used as test objects. Using a contrast-detail analysis, the probability of detection curves indicate these targets can be consistently detected within a speckle background. Our results indicate that the method has considerable promise for the semi-automated detection of abnormalities with diameters greater than a few millimeters, depending on the contrast.

Automated segmentation of transcranial sonographic images in the diagnostics of Parkinson's disease

Images captured during routine clinical transcranial sonography (TCS) examination are of a low resolution, so can be confusing for diagnostic evaluations. Manual segmentation of brain structures (areas of the midbrain and substantia nigra (SN)) that are of special interest cause inter-observer and intra-observer variability, thus restricting the reliability of Parkinson disease (PD) diagnostics. This paper presents a new technique for automated segmentation applicable to low resolution sonographic images, and particularly to brain structures related to PD. The segmentation was performed by a modified shape-based active contour (AC) segmentation algorithm. In order to suppress the speckle noise and to improve the AC segmentation, a pre-processing technique based on the averaging of adjusted spatially varying TCS images is proposed. The latter technique was tested on clinical TCS images. The results of the automated segmentation were compared with the manual markings. Two experts on the 40TCS images performed these markings. The comparison showed that an automated method is effective when segmentation of the midbrain is performed (averaged overlap between regions obtained automatically and outlined manually was 73.10±7.45%). The results of the segmentation of the SN area showed that a sufficiently correct contour of this area could also be obtained, but the accuracy of the segmentation is related to the image quality. It should be emphasised that the main difficulty in evaluating the accuracy of automated segmentation of the SN was the indefinite "gold standard" (variation between the measurements of two experts with different experience was found). And, therefore, the diagnostic reliability of the proposed technique was inconclusive.

Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery

The robust identification and measurement of the intima media thickness (IMT) has a high clinical relevance because it represents one of the most precise predictors used in the assessment of potential future cardiovascular events. To facilitate the analysis of arterial wall thickening in serial clinical investigations, in this paper we have developed a novel fully automatic algorithm for the segmentation, measurement, and tracking of the intima media complex (IMC) in B-mode ultrasound video sequences. The proposed algorithm entails a two-stage image analysis process that initially addresses the segmentation of the IMC in the first frame of the ultrasound video sequence using a model-based approach; in the second step, a novel customized tracking procedure is applied to robustly detect the IMC in the subsequent frames. For the video tracking procedure, we introduce a spatially coherent algorithm called adaptive normalized correlation that prevents the tracking process from converging to wrong arterial interfaces. This represents the main contribution of this paper and was developed to deal with inconsistencies in the appearance of the IMC over the cardiac cycle. The quantitative evaluation has been carried out on 40 ultrasound video sequences of the common carotid artery (CCA) by comparing the results returned by the developed algorithm with respect to ground truth data that has been manually annotated by clinical experts. The measured IMT(mean) ± standard deviation recorded by the proposed algorithm is 0.60 mm ± 0.10, with a mean coefficient of variation (CV) of 2.05%, whereas the corresponding result obtained for the manually annotated ground truth data is 0.60 mm ± 0.11 with a mean CV equal to 5.60%. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment and track the IMC in ultrasound CCA video sequences, and we were encouraged by the stability of our technique when applied to data captured under different imaging conditions. Future clinical studies will focus on the evaluation of patients that are affected by advanced cardiovascular conditions such as focal thickening and arterial plaques.

Using imaging-based, three-dimensional models of the cervix and uterus for studies of cervical changes during pregnancy

Preterm birth affects over 12% of all pregnancies in the United States for an annual healthcare cost of $26 billion. Preterm birth is a multifactorial disorder but cervical abnormalities are a prominent feature in many patients. Women with a short cervix are known to be at increased risk for preterm birth and a short cervix is used to target therapy to prevent preterm birth. Although the clinical significance of a short cervix is well known, the three-dimensional anatomical changes that lead to cervical shortening are poorly understood. Here, we review our previous studies of the three-dimensional anatomy of the cervix and uterus during pregnancy. The rationale for these studies was to improve our understanding of the deformation mechanisms leading to cervical shortening. Both magnetic resonance imaging and three-dimensional (3D) ultrasound were used to obtain anatomical data in healthy, pregnant volunteers. Solid models were constructed from the 3D imaging data. These solid models were used to create numerical models suitable for biomechanical simulation. Three simulations were studied: cervical funneling, uterine growth, and fundal pressure. These simulations showed that cervical changes are a complex function of the tissue properties of the cervical stroma, the loading conditions associated with pregnancy and the 3D anatomical geometry of the cervix and surrounding structures. An improved understanding of these cervical changes could point to new approaches to prevent undesired cervical shortening. This new insight should lead to therapeutic strategies to delay or prevent preterm birth.