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.     

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.     

Wavelet energy-guided level set-based active contour: A segmentation method to segment highly similar regions

This paper introduces an approach to perform segmentation of regions in computed tomography (CT) images that exhibit intra-region intensity variations and at the same time have similar intensity distributions with surrounding/adjacent regions. In this work, we adapt a feature computed from wavelet transform called wavelet energy to represent the region information. The wavelet energy is embedded into a level set model to formulate the segmentation model called wavelet energy-guided level set-based active contour (WELSAC). The WELSAC model is evaluated using several synthetic and CT images focusing on tumour cases, which contain regions demonstrating the characteristics of intra-region intensity variations and having high similarity in intensity distributions with the adjacent regions. The obtained results show that the proposed WELSAC model is able to segment regions of interest in close correspondence with the manual delineation provided by the medical experts and to provide a solution for tumour detection.     

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.     

Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.     

基于改进C-V模型的肿瘤CT图像分割

针对传统C-V模型演化速度慢和不能很好分割灰度不均匀图像的缺点,从两个方面进行了改进。首先采用一个新颖的基于局部梯度的模型,使C-V模型初始轮廓曲线快速移到目标边界附近,大大缩短了演化时间;其次,结合GVF模型从两个方向指向目标边界的特点,为C-V模型的速度方程添加一个自适应速度调节项,使模型收敛于真实边界。通过肝脏肿瘤CT图像的分割,验证该方法是有效的。     

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。     

一种眼底黄斑水肿OCT图像分割方法

基于眼底黄斑部相干光断层扫描(OCT)图像提出了分割黄斑水肿的方法。根据Chan-Vese模型,采用了一种改进的水平集算法,直接定义整数值的符号函数,曲线的外扩和内缩。通过内外轮廓线上点的相互转化实现,实现了快速分割和曲线平滑。本文用该方法对眼底黄斑水肿45张断层图像进行了分割,提取了黄斑水肿区域轮廓,取得了良好的分割效果,并估算了眼底黄斑水肿的体积,为临床诊断和治疗提供了定量分析的工具。     

A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation

Appropriate initialization and stable evolution are desirable criteria to satisfy in level set methods. In this study, a novel region-based level set method utilizing both global and local image information complementarily is proposed. The global image information is extracted from mean shift clustering without any prior knowledge. Appropriate initial contours are obtained by regulating the clustering results. The local image information, as extracted by a data fitting energy, is employed to maintain a stable evolution of the zero level set curves. The advantages of the proposed method are as follows. First, the controlling parameters of the evolution can be easily estimated by the clustering results. Second, the automaticity of the model increases because of a reduction in computational cost and manual intervention. Experimental results confirm the efficiency and accuracy of the proposed method for medical image segmentation.     

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.