Comparison of active contour models for image segmentation in X-ray coronary angiogram images

Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. However, most present snake models cannot provide better capture range and evolution stop mechanism. This project presents a new external force for active contours, largely solving both problems. An extension of the gradient vector flow snake (GVF snake) method is presented. First, the adaptive balloon force has been developed to increase the GVF snake's capture range and convergence speed. Then, a dynamic GVF force is introduced to provide an efficient evolution-stop mechanism. In this way, we prevent the snake from breaking through the correct surface and locking to other salient feature points. The active contour models have been applied on X-ray coronary angiogram images. The segmentation results demonstrate the potential of improved GVF method is comparison with all previous active contour methods. Texture parameters have been calculated and results are compared with all active contour models.     

A novel segmentation method using multiresolution analysis with 3D visualization for X-ray coronary angiogram images

Coronary angiography is a widely used tool in the diagnosis and treatment of cardiac diseases. The main cause of coronary artery disease is atherosclerosis, which leads to the narrowing of artery lumen, resulting in decreased blood supply to heart muscles. Determination of narrowing of the lumens mainly depends upon the quality of the segmented image; with improved segmentation technique there is better accuracy in identification of blocks. The main purpose of the paper is to develop an automatic, accurate segmentation technique with 3D visualization for the segmented images. 3D visualization provides clearer information regarding the shape and severity of the lesion. The thresholding technique is one of the oldest and simplest techniques used for segmentation. This paper proposes a multithresholding approach using the entropy measure and multiresolution analysis to ensure automatic and accurate segmentation by overcoming some of the problems encountered in other techniques. Also, segmentation performance analysis was conducted for various segmentation methods. This method is tested with different real coronary angiographic images and was found to perform better than the other techniques.     

A novel segmentation method using multiresolution analysis with 3D visualization for X-ray coronary angiogram images

Coronary angiography is a widely used tool in the diagnosis and treatment of cardiac diseases. The main cause of coronary artery disease is atherosclerosis, which leads to the narrowing of artery lumen, resulting in decreased blood supply to heart muscles. Determination of narrowing of the lumens mainly depends upon the quality of the segmented image; with improved segmentation technique there is better accuracy in identification of blocks. The main purpose of the paper is to develop an automatic, accurate segmentation technique with 3D visualization for the segmented images. 3D visualization provides clearer information regarding the shape and severity of the lesion. The thresholding technique is one of the oldest and simplest techniques used for segmentation. This paper proposes a multithresholding approach using the entropy measure and multiresolution analysis to ensure automatic and accurate segmentation by overcoming some of the problems encountered in other techniques. Also, segmentation performance analysis was conducted for various segmentation methods. This method is tested with different real coronary angiographic images and was found to perform better than the other techniques.     

A novel active contour model for serial image segmentation

A novel method for the segmentation of serial images is proposed. In the presented framework, the driving force acts as the attracting term to propel the evolving curve towards the object boundaries, and the adaptive term changes the sign of driving force accordingly. Therefore, the evolving curves can arrive at the desired direction without a requirement for the initial curve to be strictly inside or outside the object. A weighted length term is used to keep the smoothness of curve and penalize the formulation of discontinuities. To prevent the level set function deviating from a signed distance function, a distance rectifying flow is also added to the model; therefore the time-consuming re-initialization procedure is completely avoided. Experiments on both synthetic image and CT serial images demonstrate the feasibility and efficiency of the method.     

A novel active contour model for fully automated segmentation of intravascular ultrasound images: in vivo validation in human coronary arteries

The detection of lumen and media-adventitia borders in intravascular ultrasound (IVUS) images constitutes a necessary step for the quantitative assessment of atherosclerotic lesions. To date, most of the segmentation methods reported are either manual, or semi-automated, requiring user interaction at some extent, which increases the analysis time and detection errors. In this work, a fully automated approach for lumen and media-adventitia border detection is presented based on an active contour model, the initialization of which is performed via an analysis mechanism that takes advantage of the inherent morphologic characteristics of IVUS images. The in vivo validation of the proposed model in human coronary arteries revealed that it is a feasible approach, enabling accurate and rapid segmentation of multiple IVUS images.     

A hierarchical method based on active shape models and directed Hough transform for segmentation of noisy biomedical images; application in segmentation of pelvic X-ray images

Background

Traumatic pelvic injuries are often associated with severe, life-threatening hemorrhage, and immediate medical treatment is therefore vital. However, patient prognosis depends heavily on the type, location and severity of the bone fracture, and the complexity of the pelvic structure presents diagnostic challenges. Automated fracture detection from initial patient X-ray images can assist physicians in rapid diagnosis and treatment, and a first and crucial step of such a method is to segment key bone structures within the pelvis; these structures can then be analyzed for specific fracture characteristics. Active Shape Model has been applied for this task in other bone structures but requires manual initialization by the user. This paper describes a algorithm for automatic initialization and segmentation of key pelvic structures - the iliac crests, pelvic ring, left and right pubis and femurs - using a hierarchical approach that combines directed Hough transform and Active Shape Models.

Results

Performance of the automated algorithm is compared with results obtained via manual initialization. An error measures is calculated based on the shapes detected with each method and the gold standard shapes. ANOVA results on these error measures show that the automated algorithm performs at least as well as the manual method. Visual inspection by two radiologists and one trauma surgeon also indicates generally accurate performance.

Conclusion

The hierarchical algorithm described in this paper automatically detects and segments key structures from pelvic X-rays. Unlike various other x-ray segmentation methods, it does not require manual initialization or input. Moreover, it handles the inconsistencies between x-ray images in a clinical environment and performs successfully in the presence of fracture. This method and the segmentation results provide a valuable base for future work in fracture detection.

     

活动形状模型在医学图像分割中的应用

在医学临床实践和研究中经常需要根据各种图像对解剖结构进行识别和精确定位,以获取有用的信息。活动形状模型是计算机视觉领域成长很快的一类统计学模型方法,在图像分割和解释方面展示了相当可观的发展前景。对活动形状模型及其扩展算法活动表观模型的发展现状,以及它们在医学图像领域的应用和研究热点进行了总结并对技术的进一步发展进行了初步探讨。     

Multiscale active contour model for vessel segmentation

This paper presents a novel multiscale active contour model for vessel segmentation. The model is based on accurate analysis of the vessel structure in the image. According to different scale response of the eigenvalues of local second order derivative (Hessian matrix), a new vessel region information function, which shows a valid estimation of the vesselness measure, is defined. We introduce the posteriori probability estimation into the active contours framework and design a new objective function. The defined objective function is minimized using the variational method, and a new region-based external force is obtained, which is more accurate to the vessel structure and not sensitive to the initial condition. This active contour model combines the obtained region-based and conventional boundary-based force, which aims at finding more accurate vessel edges even when the vessel branches are low contrast or blurry. Furthermore, the proposed model is implemented by an implicit method of level set framework, the solution of which is steady and suitable for various topology changes. Moreover, two new speed functions for vessel segmentation in the level set method are presented, one for fast marching and the other for a narrow-band algorithm. The vessel segmentation experiments compared with previous geometric active contour models are shown on several medical images. The experimental results demonstrate the performance of our approach.     

Gallbladder shape extraction from ultrasound images using active contour models

Gallbladder function is routinely assessed using ultrasonographic (USG) examinations. In clinical practice, doctors very often analyse the gallbladder shape when diagnosing selected disorders, e.g. if there are turns or folds of the gallbladder, so extracting its shape from USG images using supporting software can simplify a diagnosis that is often difficult to make. The paper describes two active contour models: the edge-based model and the region-based model making use of a morphological approach, both designed for extracting the gallbladder shape from USG images. The active contour models were applied to USG images without lesions and to those showing specific disease units, namely, anatomical changes like folds and turns of the gallbladder as well as polyps and gallstones. This paper also presents modifications of the edge-based model, such as the method for removing self-crossings and loops or the method of dampening the inflation force which moves nodes if they approach the edge being determined. The user is also able to add a fragment of the approximated edge beyond which neither active contour model will move if this edge is incomplete in the USG image. The modifications of the edge-based model presented here allow more precise results to be obtained when extracting the shape of the gallbladder from USG images than if the morphological model is used.     

一种分割冠状动脉X射线造影图像的有效方法

目的 提出一种简单有效的方法进行冠状动脉X射线造影图像的分割。方法 基于Hessian矩阵的多尺度滤波和区域增长等算法,其中多尺度滤波用来增强造影图像中的血管,然后利用多种子点区域增长算法从增强后的图像中提取冠状动脉树。结果 该方法对于造影图像中血管状结构非常敏感,能够清晰提取出冠状动脉树中较细小的末梢,并能有效抑制噪声。结论 该方法适合于分割冠状动脉造影,适用于冠状动脉造影的精确量化分析。     

冠状动脉血管造影图像三维分割方法

冠状动脉计算机断层扫描血管造影技术已广泛应用于心血管疾病的诊断,冠状动脉的有效分割在冠心病诊断中起着重要的作用。本文提出一种冠状动脉血管造影图像三维分割方法,首先进行数据预处理;然后,利用分水岭算法得到子区域;最后,利用区域生长方法将分水岭子区域合并,实现冠状动脉的三维分割。本文以放射科医生手动标注结果作为标准,验证提出算法的可行性和有效性。实验结果表明,本文提出的分割方法能够有效克服冠状动脉周围组织的干扰,从而取得精确的分割结果。     

医学图像分割中的形变模型技术

形变模型技术作为一种既能结合底层图像信息，又可结合高层先验知识的图像分割技术，以其独特的魅力吸引着该领域越来越多的研究者，但至今尚未有统一的分类方法。提出一种新的分类方法，把形变模型技术大致分为自由形态和受限形态两类模型，从新的视角对形变模型技术进行了系统分类和知识梳理，回顾了形变模型技术的主要工作及其在医学图像分割领域中的应用，为研究者对这一领域的总体把握提供了有益的参考。     

Segmentation of magnetic resonance images using a combination of neural networks and active contour models

Segmentation of medical images is very important for clinical research and diagnosis, leading to a requirement for robust automatic methods. This paper reports on the combined use of a neural network (a multilayer perceptron, MLP) and active contour model ('snake') to segment structures in magnetic resonance (MR) images. The perceptron is trained to produce a binary classification of each pixel as either a boundary or a non-boundary point. Subsequently, the resulting binary (edge-point) image forms the external energy function for a snake, used to link the candidate boundary points into a continuous, closed contour. We report here on the segmentation of the lungs from multiple MR slices of the torso; lung-specific constraints have been avoided to keep the technique as general as possible. In initial investigations, the inputs to the MLP were limited to normalised intensity values of the pixels from an (7 x 7) window scanned across the image. The use of spatial coordinates as additional inputs to the MLP is then shown to provide an improvement in segmentation performance as quantified using the effectiveness measure (a weighted product of precision and recall). Training sets were first developed using a lengthy iterative process. Thereafter, a novel cost function based on effectiveness is proposed for training that allows us to achieve dramatic improvements in segmentation performance, as well as faster, non-iterative selection of training examples. The classifications produced using this cost function were sufficiently good that the binary image produced by the MLP could be post-processed using an active contour model to provide an accurate segmentation of the lungs from the multiple slices in almost all cases, including unseen slices and subjects.     

基于主动轮廓的三维气管树自动分割方法

在计算机辅助手术领域,对CT图像中肺部区域的气管和支气管进行准确分割和提取具有重要意义。气管和支气管的分割提取有助于手术的导航参考,准确的提取结果有助于避免手术中对支气管的损伤。本文设计了一种全自动的三维肺气管分割算法:首先,将基于主动轮廓的GCS方法引入到肺气管分割当中,在三维图像中得到粗分割结果;然后,利用灰度重建的方法将粗分割结果中分离的部分变得连通;最后利用三维区域生长提取支气管树。实验结果表明,本文方法能够稳定地分割出不同病例的支气管树,与自动阈值区域生长分割结果相比较,本文结果在支气管分叉数上有很大的提升,最少提升有28%,最多达86%。     

Optimal femoral head contour segmentation in CT images using dynamic programming.

We present a new contour segmentation method for femoral head detection in CT images. The principal idea underlying our approach is to represent the contour using active rays. Each ray has internal energy (continuity and smoothness constraints), as well as external energy (image-oriented edges and global a-priori knowledge). The causal nature of this representation allows a globally optimal, non-iterative solution to be found using dynamic programming. Moreover, even pathological bone structures can be automatically segmented. Several clinical situations on pelvis CT scan with hip degenerative change shows that this approach is more efficient than conventional methods. In this paper, we describe our method and discuss how it can be used to create graphical 3D models of the hip that are suitable for preoperative planning.     

基于自注意力的双通路全脊柱 X 光图像分割模型

全脊柱X光图像（包含脊柱、骶骨及髂骨）分割是目前脊椎疾病智能诊断中首要关键的环节。针对U-Net语义分割算法在全脊柱X光图像多区域分割精度较差的问题,提出一种双通道语义分割算法DAU-Net,通过空间通道与语义通道分别学习空间信息特征与图像语义特征,并在解码器端对两类特征进行融合,获取脊柱X光图像中更精准的分割边界。在空间通道中,使用空洞卷积及残差模块扩大视野域并保留更多远端特征信息。此外,将自注意力机制引入语义通道,并设计不同的自注意力编码与自注意力解码模块构建全局关联信息,实现对多个目标骨骼区域语义分割。实验结果表明,DAU-Net能够有效提高脊柱X光图像上的分割精度,相比U-Net、ResU-Net、Attention U-Net、U-Net++,Dice系数分别提高4.00%、1.90%、4.60%、1.19%。     

基于增强CT的手动勾画与基于PET-CT的自动勾画方法在鼻咽癌放疗中的对比研究

目的:探究基于增强CT的手动勾画与基于PET-CT的自动勾画方法在鼻咽癌放疗中的优劣性及应用价值。方法:采集34例鼻咽癌患者的PET-CT数据,比较医生分别在增强CT和PET-CT图像上勾画靶区的差异性,以CSCO指南在PET-CT双模态融合图像上手动勾画的靶区作为金标准,应用Matlab软件计算单一标准摄取值（SUV）阈值分割法和MIM软件自带的PET-EDGE分割法两者的重叠率Dice和豪斯多夫距离HD值,并采用t检验进行统计学分析。结果:基于增强CT图像的手动勾画法、基于PET-CT图像的单一SUV阈值法以及PET-EDGE自动勾画法,3者靶区分割结果的Dice值分别为0.706、0.736、0.806,HD值分别为12.601、11.631、6.143 mm。以PET-CT双模态图像作为参考的靶区手动勾画法与基于增强CT的手动勾画存在差异（Dice:P=8.33×10-7;HD:P=1.065×10-18）;MIM软件自带的PET-EDGE靶区分割法Dice和HD值高于单一SUV阈值法,差异具有统计学意义（Dice:P=0.019 8;HD:P=0.013 7）。结论:在鼻咽癌肿瘤靶区的分割性能上,基于PET-CT图像的自动勾画优于基于增强CT图像的手动勾画,MIM软件自带的PET-EDGE分割法优于单一SUV阈值分割法。     

Homogeneity- and density distance-driven active contours for medical image segmentation

In this paper, we present a novel active contour (AC) model for medical image segmentation that is based on a convex combination of two energy functionals to both minimize the inhomogeneity within an object and maximize the distance between the object and the background. This combination is necessary because objects in medical images, e.g., bones, are usually highly inhomogeneous while distinct organs should generate distinct image configurations. Compared with the conventional Chan–Vese AC, the proposed model yields similar performance in a set of CT images but performs better in an MRI data set, which is generally in lower contrast.     

Detection of the intima and media layer thickness of ultrasound common carotid artery image using efficient active contour segmentation technique

An active contour segmentation technique for extracting the intima–media layer of the common carotid artery (CCA) ultrasound images employing semiautomatic region of interest identification and speckle reduction techniques is presented in this paper. An attempt has been made to test the ultrasound images of the carotid artery of different subjects with this contour segmentation based on improved dynamic programming method. It is found that the preprocessing of ultrasound images of the CCA with region identification and despeckleing followed by active contour segmentation algorithm can be successfully used in evaluating the intima–media thickness (IMT) of the normal and abnormal subjects. It is also estimated that the segmentation used in this paper results an intermethod error of 0.09 mm and a coefficient of variation of 18.9%, for the despeckled images. The magnitudes of the IMT values have been used to explore the rate of prediction of blockage existing in the cerebrovascular and cardiovascular pathologies and also hypertension and atherosclerosis.     

基于三维冠状动脉CTA图像的半自动血管分割方法

目的从患者胸腔部位的计算机断层血管造影(computed tomography angiongraphy,CTA)图像中分离出冠状动脉血管,并实现三维的可视化,以便医生对由冠状动脉病变引起的心血管疾病进行诊断和治疗。方法在传统区域生长的基础上提出一种半自动的血管分割方法。首先采用基于灰度值的主动脉预生长,然后进行基于自适应阈值的冠状动脉生长,最后进行后期处理,得到最终分割结果。结果通过这种半自动的区域生长得到的结果,在三维上进行可视化后能够较清晰地判断出血管的粗细、形状、狭窄等情况,且得到的模型也可用于后期的相关计算。结论基于传统区域生长算法的半自动血管分割方法提高了冠状动脉分割的适应性和有效性,能更好地对冠状动脉CTA图像进分割提取。