3-D breast ultrasound segmentation using active contour model

In this study, we made use of the discrete active contour model to overcome the natural properties of ultrasound (US) images, speckle, noise and tissue-related textures, to segment the breast tumors precisely. Determination of the real tumor boundary with the snake-deformation process requires an initial contour estimate. However, the manual way to sketch an initial contour is very time-consuming. Thus, we propose an automatic initial contour-finding method that not only maintains the tumor shape, but also is close to the tumor boundary and inside the tumor. During the deformation process, to prevent the snake trapping into the false position caused by tissue-related texture or speckle, we added the edge information as an image feature to define the external force. In addition, because the 3-D volume of a tumor is essentially constructed by a sequence of 2-D images, our method for finding boundaries of a tumor can be extended to 3-D cases. By precisely counting the volume of the 3-D images, we can get the volume of tumor. Finally, we will show that the proposed techniques have rather good performance and lead to a satisfactory result in comparison with the estimated volume and physician's estimate.     

Image-based cardiac gating for three-dimensional intravascular ultrasound imaging

Three-dimensional (3-D) intravascular ultrasound (US), or IVUS, provides valuable insight into the tissue characteristics of the coronary wall and plaque composition. However, artefacts due to cardiac motion and vessel wall pulsation limit the accuracy and variability of coronary lumen and plaque volume measurement in 3-D IVUS images. ECG-gated image acquisition can reduce these artefacts but it requires recording the ECG signal and may increase image acquisition time. The goal of our study was to reconstruct a 3-D IVUS image with negligible cardiac motion and vessel pulsation artefacts, by developing an image-based gating method to track 2-D IVUS images over the cardiac cycle. Our approach involved selecting 2-D IVUS images belonging to the same cardiac phase from an asynchronously-acquired series, by tracking the changing lumen contour over the cardiac cycle. The algorithm was tested with IVUS images of a custom-built coronary vessel phantom and with patient images. The artefact reduction achieved using the image-gating approach was > 86% in the in vitro images and > 80% in the in vivo images in our study. Our study shows that image-based gating of IVUS images provides a useful method for accurate reconstruction of 3-D IVUS images with reduced cardiac motion artefact.     

计算机辅助设计和快速成型技术制造颜面萎缩衬垫物的可能性

背景:半侧颜面萎缩畸形的整复方法较多,以往常通过制备患者的石膏面模,在上面堆蜡来恢复患者的面形,蜡型用作手术中衬垫的参考.但由于畸形的变化多,矫治设计的难度大,矫治效果不甚理想.目的:探讨应用计算机辅助设计和快速成型技术制造用于矫正半侧颜面萎缩等凹陷畸形衬垫物的可能性.方法:对半侧颜面萎缩患者行螺旋CT扫描,利用工作站进行扫描图像的容积三维重建后,重新间隔分层,利用CuteFTP 4.0软件以BMP格式下载.应用课题组自主开发的CT图像处理软件对已下载的二维图像进行过滤、筛减、降噪、校正失真等处理,对图像的边缘轮廓进行提取,得到面颅骨皮质骨边缘轮廓的矢量化线图,将该线图数据输入Surfacer 9.0重建软件,对轮廓曲线进行矢量叠加,从而得到面颅骨的三维三角形面片线框模型及实体模型.将健侧面颅骨的点云数据按镜像对称变换到患侧,这样在患侧骨和健侧镜像之间就形成了充填物的三维模型,为补偿软组织的萎缩,将其外表面点云数据外移1.5 mm.对CAD后的三维Surfacer数据重新分层,在RpDataRepare中完成充填物的轮廓编辑和成型的支撑设置,形成RP项目文件,输出快速原型所需的加工文件.par,制造出衬垫物模板,作为实施手术过程中的参照.结果与结论:获得了患者颅面骨表面轮廓的三维实体模型,并由计算机辅助设计,快速成型制造出衬垫物模板,并以此为参照完成手术,效果满意.说明应用快速成型技术可以完成半侧颜面萎缩等凹陷畸形的衬垫物的制造,精度高,快捷,在颅颌面外科假体的个体化制造方面有良好的应用前景.     

3-D US frame positioning using speckle decorrelation and image registration

In this paper, a new positioning system is proposed for the 3-D ultrasound (US). This system combines the image registration technique and speckle decorrelation algorithm to accurately position sequential ultrasonic images without any additional positioning hardware. The speckle decorrelation algorithm estimates the relative distance of two neighboring frames and the image registration technique gets the range of the whole 3-D ultrasonic data set and makes slight modification on each frame's position. The image registration technique is based on the reference image, which is perpendicular to the 3-D ultrasonic data set. This reference image intersects each frame of the 3-D ultrasonic data set in a line. For each frame, the intersectional line is first found and then the location in the reference image can be used to estimate the position of this frame. This system uses the data set of consecutive 2-D freehand-scanned US B-mode images to construct the 3-D US volume data, and it can be integrated into the 3-D US volume rendering system.     

A three-dimensional,anatomically detailed foot model: a foundation for a finite element simulation and means of quantifying foot-bone position

We generated an anatomically detailed, three-dimensional (3-D) reconstruction of a human foot from 286 computerized topographic (CT) images. For each bone, 2-D cross-sectional data were obtained and aligned to form a stacked image model. We calculated the inertial matrix of each bone from the stacked image model and used it to determine the principal axes. Relative angles between the principal axes of the bones were employed to describe the shape of the foot, i.e., the relationships between the bones of the foot. A 3-D surface model was generated from the stacked image models and a detailed 3-D mesh for each bone was created. Additionally, the representative geometry of the plantar soft tissue was obtained from the CT scans, while the geometries of the cartilage between bones were obtained from the 3-D surface bone models. This model served dual purposes: it formed the anatomical foundation for a future finite element model of the human foot and we used it to objectively quantify foot shape using the relationships between the principal axes of the foot bones.     

Model tags: direct three-dimensional tracking of heart wall motion from tagged magnetic resonance images

Although magnetic resonance tissue tagging is a useful tool for the non-invasive measurement of three-dimensional (3-D) heart wall motion, the clinical utility of current analysis techniques is limited by the prohibitively long time required for image analysis. A method was therefore developed for the reconstruction of 3-D heart wall motion directly from tagged magnetic resonance images, without prior identification of ventricular boundaries or tag stripe locations. The method utilized a finite-element model to describe the shape and motion of the heart. Initially, the model geometry was determined at the time of tag creation by fitting a small number of guide points which were placed interactively on the images. Model tags were then created within the model as material surfaces which defined the location of the magnetic tags. An objective function was derived to measure the degree of match between the model tags and the image stripes. The objective was minimized by allowing the model to deform directly under the influence of the images, utilizing an efficient method for calculating image-derived motion constraints. The model deformation could also be manipulated interactively by guide points. Experiments were performed using clinical images of a normal volunteer, as well as simulated images in which the true motion was specified. The root-mean-squared errors between the known and calculated displacement and strain for the simulated images were similar to those obtained using previous stripe-tracking and model-fitting methods. A significant improvement in analysis time was obtained for the normal volunteer and further improvements may allow the method to be applied in a 'real-time' clinical environment.     

Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images

New automated whole breast ultrasound (ABUS) machines have recently been developed and the ultrasound (US) volume dataset of the whole breast can be acquired in a standard manner. The purpose of this study was to develop a novel computer-aided diagnosis system for classification of breast masses in ABUS images. One hundred forty-seven cases (76 benign and 71 malignant breast masses) were obtained by a commercially available ABUS system. Because the distance of neighboring slices in ABUS images is fixed and small, these continuous slices were used for reconstruction as three-dimensional (3-D) US images. The 3-D tumor contour was segmented using the level-set segmentation method. Then, the 3-D features, including the texture, shape and ellipsoid fitting were extracted based on the segmented 3-D tumor contour to classify benign and malignant tumors based on the logistic regression model. The Student’s t test, Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. From the Az values of ROC curves, the shape features (0.9138) are better than the texture features (0.8603) and the ellipsoid fitting features (0.8496) for classification. The difference was significant between shape and ellipsoid fitting features (p = 0.0382). However, combination of ellipsoid fitting features and shape features can achieve a best performance with accuracy of 85.0% (125/147), sensitivity of 84.5% (60/71), specificity of 85.5% (65/76) and the area under the ROC curve Az of 0.9466. The results showed that ABUS images could be used for computer-aided feature extraction and classification of breast tumors. (E-mail: rfchang@csie.ntu.edu.tw)     

A framework for automatic analysis of the dynamic behaviour of coronary angiograms

A framework for coronary vessels analysis in digital subtracted angiograms is described. This method combines the motion estimation with the frame-to-frame structure detection in a natural way such that they act interactively. The first step consists of the extraction of the vessel centrelines in one image and their organization into meaningful constituents or branches of the coronary arterial tree. The motion is then estimated along the centrelines through a gradient based method. These motion estimates supply an initial positioning of an active contour model (or ‘snake“) in the next image. This model adapts itself by changing its shape to accurately fit onto the new centrelines. This process is then reiterated on the subsequent images to depict the dynamic behaviour of all the relevant branches. The main interests of this scheme are: (1) the active models operate locally so a fast detection of the vessels can be performed; (2) the centrelines extraction is fully guided by the confluence of the motion estimation and the contour model; (3) both morphological and kinetic features are provided on a quantitative basis.     

Registration-based segmentation of three-dimensional ultrasound images for quantitative measurement of fetal craniofacial structure

Segmentation of a fetal head from three-dimensional (3-D) ultrasound images is a critical step in the quantitative measurement of fetal craniofacial structure. However, two main issues complicate segmentation, including fuzzy boundaries and large variations in pose and shape among different ultrasound images. In this article, we propose a new registration-based method for automatically segmenting the fetal head from 3-D ultrasound images. The proposed method first detects the eyes based on Gabor features to identify the pose of the fetus image. Then, a reference model, which is constructed from a fetal phantom and contains prior knowledge of head shape, is aligned to the image via feature-based registration. Finally, 3-D snake deformation is utilized to improve the boundary fitness between the model and image. Four clinically useful parameters including inter-orbital diameter (IOD), bilateral orbital diameter (BOD), occipital frontal diameter (OFD) and bilateral parietal diameter (BPD) are measured based on the results of the eye detection and head segmentation. Ultrasound volumes from 11 subjects were used for validation of the method accuracy. Experimental results showed that the proposed method was able to overcome the aforementioned difficulties and achieve good agreement between automatic and manual measurements.     

Semiautomated thermal lesion segmentation for three-dimensional elastographic imaging

Several studies have demonstrated that lesion volumes computed from multiple planar slices through the region-of-interest (ROI) are more accurate than volumes estimated assuming simple shapes and incorporating single or orthogonal diameter estimates. However, manual delineation of boundaries on multiple planar 2-D images is tedious and labor-intensive. Automatic extraction of lesion boundaries is, therefore, attractive and imperative to remove subjectivity and reduce assessment time. This paper presents a semiautomated segmentation algorithm for thermal lesions on 3-D elastographic data to obtain both area and volume information. The semiautomated segmentation algorithm is based on thresholding and morphologic opening of both 2-D and 3-D elastographic data. Results obtained on 44 thermal lesions imaged in vitro using elastography were compared to manual delineation of both elastographic and pathology images. Results obtained using semiautomated segmentation demonstrate a close correspondence with manual delineation results. However, area and volume estimates obtained using both manual and semiautomated segmentation of lesions seen on elastograms slightly underestimate areas and volumes measured from pathology.     

A comparison of three-dimensional ultrasound, two-dimensional ultrasound and dissections for determination of lesion volume in tendons

The purpose of this work was to evaluate the accuracy and precision of a freehand three-dimensional (3-D) ultrasonography system in the determination of lesion volume in tendons. The accuracy and precision of a 3-D ultrasonography system was assessed by performing repeated measurements on a phantom of known volume. Volume measurements of tendon lesions performed with 3-D ultrasonography were compared with measurements based on a series of two-dimensional (2-D) ultrasound (US) scans and to direct measurements from dissections. A novel method for the creation of tendon lesions in vitro was developed. 3-D US showed excellent precision and accuracy in measurements of the phantom (mean measured volume = 3.76 mL, calculated volume = 3.77 mL, coefficient of variation (CoV) = 0.54%) and good repeatability in the determination of tendon lesions (repeatability coefficient = 0.00047). All three methods examined were repeatable (repeatability coefficient for 2-D US = 0.00032, repeatability coefficient for dissections = 0.00076). However, each of the methods produced different results and no constant relationship could be found between any of the measurement methods. Both 3-D and 2-D US proved to be repeatable techniques for the measurement of the volume of a tendon lesion. Even if they produced different results, each of them can be repeatedly used individually. It was not possible to define which one provided the most accurate value as a result of difficulties encountered in lesion identification on histology, and therefore the lack of a gold standard.     

Segmentation in Echocardiographic Sequences using Shape-based Snake Model Combined with Generalized Hough Transformation

A novel method for segmentation of cardiac structures in temporal echocardiographic sequences based on the snake model is presented. The method is motivated by the observation that the structures of neighboring frames have consistent locations and shapes that aid in segmentation. To cooperate with the constraining information provided by the neighboring frames, we combine the template matching with the conventional snake model. It means that the model not only is driven by conventional internal and external forces, but also combines an additional constraint, the matching degree to measure the similarity between the neighboring prior shape and the derived contour. Furthermore, in order to auto or semi-automatically segment the sequent images without manually drawing the initial contours in each image, generalized Hough transformation (GHT) is used to roughly estimate the initial contour by transforming the neighboring prior shape. The method is particularly useful in case of the large frame-to-frame displacement of structure such as mitral valve. As a result, the active contour can easily detect the desirable boundaries in ultrasound images and has a high penetrability through the interference of various undesirables, such as the speckle, the tissue-related textures and the artifacts.     

Quantitative evaluation of diagnostic information around the contours in ultrasound images

Purpose To develop a new contour extraction method for identifying abnormal tissue. Methods We combined two techniques: logarithmic K distribution of a scattering model (method 1) and regional discrimination using the characteristics of local ultrasound images (method 2) into an integrated method (method 3) that provides accurate contours, which are essential for quantitizing border information. Results The diagnostic tissue information around the border of an image can be characterized by its shape and texture statistics. The degrees of circularity and irregularity and the depth–width ratio were calculated for the extracted contours of breast tumors. In addition, gradients, separability, and variance between the two regions along the contour and the area and variance of the internal echoes, were calculated as indices of diagnostic criteria of breast tumors. The quantitized indices were able to discriminate among cysts, fibroadenomas, and cancer. Conclusion In many ultrasound images of breast tumors, the combined techniques, the variance ratio of the logarithmic K distribution to the logarithmic Rayleigh distribution and the multilevel technique with local image information can effectively extract abnormal tissue contours.     

Analysis of refractive artifacts by reconstructed three-dimensional ultrasound imaging

Purpose Refractive artifacts are frequently encountered in clinical settings, and they have been analyzed on the basis of conventional two-dimensional (2-D) ultrasound (US) images, but this method is restricted to monoplane data and is limited by its inability to assess the three-dimensional (3-D) structure of refractive artifacts. The aim of this study was to evaluate the role of reconstructed 3-D US images in the analysis of refractive artifacts. Methods The following representative refractive artifacts were analyzed on the basis of reconstructed 3-D US images: (a) a distorted image of a fine tube behind a cyst (balloon); (b) a deformed image of the bottom of a balloon; and (c) a duplication artifact due to the acoustic lens effect. Results (a) A tube was imaged as a fine echogenic line with two points of sudden interruption, unlike a curved needle, which was imaged without interruption. (b) 3-D US allowed us to visualize the mode of deformity in the image of the bottom of a fluid-filled balloon in a water bath. When the acoustic velocity in the fluid was greater than that in the surrounding water, the bottom of the balloon appeared to be shrunken. When the acoustic velocity in the fluid was less than that in the surrounding water, the bottom of the balloon appeared to be swollen. (c) When we placed two pieces of white chicken meat in front of a fine needle, the needle was duplicated in the resulting image. In this case, the needle appeared to be vague and fuzzy. In this case, 3-D US did not add further information to the 2-D images. Conclusions Our study suggests that reconstructed 3-D US images provide a better understanding of the mode of refractive artifacts than do 2-D US images.     

Interactive segmentation of abdominal aortic aneurysms in CTA images

A model-based approach to interactive segmentation of abdominal aortic aneurysms from CTA data is presented. After manual delineation of the aneurysm sac in the first slice, the method automatically detects the contour in subsequent slices, using the result from the previous slice as a reference. If an obtained contour is not sufficiently accurate, the user can intervene and provide an additional manual reference contour. The method is inspired by the active shape model (ASM) segmentation scheme (), in which a statistical shape model, derived from corresponding landmark points in manually labeled training images, is fitted to the image in an iterative manner. In our method, a shape model of the contours in two adjacent image slices is progressively fitted to the entire volume. The contour obtained in one slice thus constrains the possible shapes in the next slice. The optimal fit is determined on the basis of multi-resolution gray level models constructed from gray value patches sampled around each landmark. We propose to use the similarity of adjacent image slices for this gray level model, and compare these to single-slice features that are more generally used with ASM. The performance of various image features is evaluated in leave-one-out experiments on 23 data sets. Features that use the similarity of adjacent image slices outperform measures based on single-slice features in all cases. The average number of slices in our datasets is 51, while on average eight manual initializations are required, which decreases operator segmentation time by a factor of 6.     

Breast cancer diagnosis using three-dimensional ultrasound and pixel relation analysis

Because ultrasound (US) imaging offers benefits compared with other medical imaging techniques, it is used routinely in nearly all hospitals and many clinics. However, the surface features and internal structure of a tumor are not easily demonstrated simultaneously using the traditional 2-D US. The newly developed three-dimensional (3-D) US can capture the morphology of a breast tumor and overcome the limitations of the traditional 2-D US. This study deals with pixel relation analysis techniques for use with 3-D breast US images and compares its performance to 2-D versions of the images. The 3-D US imaging was performed using a Voluson 530 scanner. The rectangular subimages of the volume-of-interest (VOI) were manually selected and the selected VOIs were outlined to include the entire extent of the tumor margin. The databases in this study included 54 malignant and 161 benign tumors. All solid nodules at US belong over C3 (probably benign) according to ACR BI-RADS category. All or some selected 2-D slices were used separately to calculate the diagnosis features for a 3-D US data set. We have proposed and compared several different methods to extract the characteristics of these consecutive 2-D images. As shown in our experiments, the diagnostic results were better than those of the conventional 2-D US. In the experiments, the area index Az under ROC curve of the proposed 3-D US method can achieve 0.9700 +/- 0.0118, but Az of the 2-D US is only 0.8461 +/- 0.0315. The p value of these two Az differences using z test is smaller than 0.01. Furthermore, we can find that the features from only several slices are enough to provide good diagnostic results if the adopted features are modified from the 2-D features.     

Fast image mapping of endoscopic image mosaics with three-dimensional ultrasound image for intrauterine fetal surgery

This paper describes a fast image mapping system that integrates endoscopic image mosaics with three-dimensional (3-D) ultrasound images for assisting intrauterine laser photocoagulation treatment. Endoscopic laser photocoagulation treatment has a good survival rate and a low complication rate for twins. However, the small field of view and lack of surrounding information makes the identification of vessel anastomosis difficult. We have developed an extended visualization system with the fusion of endoscopic image mosaics with a 3-D ultrasound-image model. Fully automatic and fast calibration is used for endoscope calibration in fluid. The 3-D spatial position of the endoscopic images and the ultrasound image are tracked by a 3-D position tracking device. The mosaiced endoscope images are registered to the surface of the 3-D ultrasound placenta model by using a fast GPU-based image rendering method and a seamless multi-images processing. Experimental results show that the system may provide an improved and efficient way of planning and guidance in laser photocoagulation treatment.     

Segmentation of the heart muscle in 3-D pediatric echocardiographic images

This study aimed to show segmentation of the heart muscle in pediatric echocardiographic images as a preprocessing step for tissue analysis. Transthoracic image sequences (2-D and 3-D volume data, both derived in radiofrequency format, directly after beam forming) were registered in real time from four healthy children over three heart cycles. Three preprocessing methods, based on adaptive filtering, were used to reduce the speckle noise for optimizing the distinction between blood and myocardium, while preserving the sharpness of edges between anatomical structures. The filtering kernel size was linked to the local speckle size and the speckle noise characteristics were considered to define the optimal filter in one of the methods. The filtered 2-D images were thresholded automatically as a first step of segmentation of the endocardial wall. The final segmentation step was achieved by applying a deformable contour algorithm. This segmentation of each 2-D image of the 3-D+time (i.e., 4-D) datasets was related to that of the neighboring images in both time and space. By thus incorporating spatial and temporal information of 3-D ultrasound image sequences, an automated method using image statistics was developed to perform 3-D segmentation of the heart muscle.     

全膝关节假体三维有限元模型的建立

背景：由于全膝关节的结构形态具有运动复杂、受力复杂等特性,造成了数据采集,模型建立困难,影响了三维实体模型的准确性。目的：建立全膝关节假体三维有限元模型。方法：通过MicroscribeG2三维定位扫描仪取得假体数据、Geomagic软件进行曲面拟合、导入大型有限元分析软件Abaqus6．7．2建立全膝关节假体三维有限元模型,对如何提高三维有限元模型的精确性,扩大模型的开放性以及提高建模效率,增加关节外科医师在建模过程中的参与性进行探讨。结果与结论：通过MicroscribeG2三维定位扫描仪取得假体数据、Geomagic软件进行曲面拟合、导入大型有限元分析软件Abaqus6．7．2建立了全膝关节假体三维有限元模型。与以往建模方法比较,该模块设计使模型更加精准,使用更灵活,简化了有限元前期处理过程,明显降低了建模难度,提高了建模效率,增加了模型的扩展形,并获得了更高的精度。     

全膝关节假体三维有限元模型的建立

背景:由于全膝关节的结构形态具有运动复杂、受力复杂等特性,造成了数据采集,模型建立困难,影响了三维实体模型的准确性。目的:建立全膝关节假体三维有限元模型。方法:通过Microscribe G2三维定位扫描仪取得假体数据、Geomagic软件进行曲面拟合、导入大型有限元分析软件Abaqus6.7.2建立全膝关节假体三维有限元模型,对如何提高三维有限元模型的精确性,扩大模型的开放性以及提高建模效率,增加关节外科医师在建模过程中的参与性进行探讨。结果与结论:通过Microscribe G2三维定位扫描仪取得假体数据、Geomagic软件进行曲面拟合、导入大型有限元分析软件Abaqus6.7.2建立了全膝关节假体三维有限元模型。与以往建模方法比较,该模块设计使模型更加精准,使用更灵活,简化了有限元前期处理过程,明显降低了建模难度,提高了建模效率,增加了模型的扩展形,并获得了更高的精度。