Multiphase Active Contour Research Articles

A multiphase texture-based model of active contours assisted by a convolutional neural network for automatic CT and MRI heart ventricle segmentation

Background: Left and right ventricle automatic segmentation remains one of the more important tasks in computed aided diagnosis. Active contours have shown to be efficient for this task, however they often require user interaction to provide the initial position, which drives the tool substantially dependent on a prior knowledge and a manual process. Methods: We propose to overcome this limitation with a Convolutional Neural Network (CNN) to reach the assumed target locations. This is followed by a novel multiphase active contour method based on texture that enhances whole heart patterns leading to an accurate identification of distinct regions, mainly left (LV) and right ventricle (RV) for the purposes of this work. Results: Experiments reveal that the initial location and estimated shape provided by the CNN are of great concern for the subsequent active contour stage. We assessed our method on two short data sets with Dice scores of 93% (LV-CT), 91% (LV-MRI), 0.86% (RV-CT) and 0.85% (RV-MRI). Conclusion: Our approach overcomes the performance of other techniques by means of a multiregion segmentation assisted by a CNN trained with a limited data set, a typical issue in medical imaging.

An active contour model for brain magnetic resonance image segmentation based on multiple descriptors

With the increasing use of surgical robots, robust and accurate segmentation techniques for brain tissue in the brain magnetic resonance image are needed to be embedded in the robot vision module. However, the brain magnetic resonance image segmentation results are often unsatisfactory because of noise and intensity inhomogeneity. To obtain accurate segmentation of brain tissue, one new multiphase active contour model, which is based on multiple descriptors mean, variance, and the local entropy, is proposed in this study. The model can bring about a more full description of local intensity distribution. Also, the entropy is introduced to improve the performance of robustness to noise of the algorithm. The segmentation and bias correction for brain magnetic resonance image can be simultaneously incorporated by introducing the bias factor in the proposed approach. At last, three experiments are carried out to test the performance of the method. The results in the experiments show that method proposed in this study performed better than most current methods in regard to accuracy and robustness. In addition, the bias-corrected images obtained by proposed method have better visual effect.

Self-organizing neural networks for image segmentation based on multiphase active contour

Image segmentation is a process of segregating foreground object from background object in an image. This paper proposes a method to perform image segmentation for the color and textured images with a two-step approach. In the first step, self-organizing neurons based on neural networks are used for clustering the input image, and in the second step, multiphase active contour model is used to get various segments of an image. The contours are initialized in the active contour model with the help of the self-organizing maps obtained as a result of first step. From the results, it is inferred that the proposed method provides better segmentation result for all types of images.

An Integrated Active Contour Approach to Shoreline Mapping Using HSI and DEM

This paper introduces a new approach to shoreline mapping, which provides critical input to nautical charting, coastal zone management, and legal boundary determination. We extract shorelines by fusing AVIRIS hyperspectral imagery (HSI) with a LiDAR-generated DEM using a multiphase active contour segmentation technique. Our approach employs a study of object spectra and a knowledge-based segmentation scheme for generating an initial solution followed by a contour evolution technique to achieve a subpixel level of accuracy, while maintaining low computational complexity. Introducing the DEM into shoreline delineation from HSI proves to be a useful tool in eliminating misclassifications and in increasing localization accuracy. Experimental results show that our integrated approach to mapping shorelines has promising outcomes that provide a way to exploit the rich information found in HSI.

Separation of overlapping bacilli in microscopic digital TB images

The sputum smear microscopy based tuberculosis (TB) screening method is a conventional method employed for disease identification. It provides significant benefit to TB burdened communities across the globe; however, there are many challenges faced in processing the sputum smear images. When the smear is thick or uneven the number of overlapping bacilli is more which impedes the diagnosis. The separation of overlapping bacilli is significant without which the results lead to gross errors in identification of the disease causing agent. In this work, separation of overlapping bacilli is carried out by method of concavity (MOC) and is compared with the conventional methods such as multi-phase active contour (MAC) and marker-controlled watershed (MCW). Performance of the methods is evaluated based on the statistical mean quality score of shape descriptors extracted from the separated and existing true bacilli. The shape descriptors employed in this work include geometric features, Hu's, Zernike moments and Fourier descriptors. Results of separated overlapping bacilli demonstrate that MOC performs better than MAC and MCW. It is observed that the statistical mean quality score of the separated bacilli using the proposed MOC shows nearest match with true bacilli. The validation performed with experimental results to that of human annotations highlights the performance of MOC in separating the overlapping bacilli in the sputum smear images.

Multiphase B-spline level set and incremental shape priors with applications to segmentation and tracking of left ventricle in cardiac MR images

This paper presents a new multiphase active contour model for object segmentation and tracking. The paper introduces an energy functional which incorporates image feature information to drive contours toward desired boundaries, and shape priors to constrain the evolution of the contours with respect to reference shapes. The shape priors, in the model, are constructed by performing the incremental principal component analysis (iPCA) on a set of training shapes and newly available shapes which are the resulted shapes derived from preceding segmented images. By performing iPCA, the shape priors are updated without repeatedly performing PCA on the entire training set including the existing shapes and the newly available shapes. In addition, by incrementally updating the resulted shape information of consecutive frames, the approach allows to encode shape priors even when the database of training shapes is not available. Moreover, in shape alignment steps, we exploit the shape normalization procedure, which takes into account the affine transformation, to directly calculate pose transformations instead of solving a set of coupled partial differential equations as in gradient descent-based approaches. Besides, we represent the level set functions as linear combinations of continuous basic functions expressed on B-spline basics for a fast convergence to the segmentation solution. The model is applied to simultaneously segment/track both the endocardium and epicardium of left ventricle from cardiac magnetic resonance (MR) images. Experimental results show the desired performances of the proposed model.

Fast and globally convex multiphase active contours for brain MRI segmentation

Multiphase active contour based models are useful in identifying multiple regions with spatial consistency but varying characteristics such as the mean intensities of regions. Segmenting brain magnetic resonance images (MRIs) using a multiphase approach is useful to differentiate white and gray matter tissue for anatomical, functional and disease studies. Multiphase active contour methods are superior to other approaches due to their topological flexibility, accurate boundaries, robustness to image variations and adaptive energy functionals. Globally convex methods are furthermore initialization independent. We extend the relaxed globally convex Chan and Vese two-phase piecewise constant energy minimization formulation of Chan et al. (2006) [1] to the multiphase domain and prove the existence of a global minimizer in a specific space which is one of the novel contributions of the paper. An efficient dual minimization implementation of our binary partitioning function model accurately describes disjoint regions using stable segmentations by avoiding local minima solutions. Experimental results indicate that the proposed approach provides consistently better accuracy than other related multiphase active contour algorithms using four different error metrics (Dice, Rand Index, Global Consistency Error and Variation of Information) even under severe noise, intensity inhomogeneities, and partial volume effects in MRI imagery.

A Morphological Multiphase Active Contour for Vascular Segmentation

This paper presents a morphological active contour ideal for vascular segmentation in biomedical images. The unenhanced images of vessels and background are successfully segmented using a two-step morphological active contour based upon Chan and Vese’s Active Contour without Edges. Using dilation and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust alternative to solving partial differential equations used by traditional level-set Active Contour models. The proposed method is demonstrated with segmented data set images and compared to results garnered from multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.

A novel multiphase active contour model for inhomogeneous image segmentation

The problem of image segmentation has been investigated with a focus on inhomogeneous multiphase image segmentation. Intensity inhomogeneity is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) and natural images segmentation. The complex images usually contain an arbitrary number of objects. This paper presents a new multiphase active contour model method for simultaneous regions classification of MR images and natural images without bias field correction. In this model, a simple and effective initialization method is taken to speed up the curve evolution toward final results; a new multiphase level set method is proposed to segment the multiple regions. This model not only extracts multiple objects simultaneously, but also provides smooth and accurate boundaries of the objects. The results for experiments on several synthetic and real images demonstrate the effectiveness and accuracy of our model.

Semi-automatic detection of linear archaeological traces from orthorectified aerial images

This paper presents a semi-automatic approach for archaeological traces detection from aerial images. The method developed was based on the multiphase active contour model (ACM). The image was segmented into three competing regions to improve the visibility of buried remains showing in the image as crop marks (i.e. centuriations, agricultural allocations, ancient roads, etc.). An initial determination of relevant traces can be quickly carried out by the operator by sketching straight lines close to the traces. Subsequently, tuning parameters (i.e. eccentricity, orientation, minimum area and distance from input line) are used to remove non-target objects and parameterize the detected traces. The algorithm and graphical user interface for this method were developed in a MATLAB environment and tested on high resolution orthorectified aerial images. A qualitative analysis of the method was lastly performed by comparing the traces extracted with ancient traces verified by archaeologists.

Localized Graph-cuts Based Multiphase Active Contour Model for Image Segmentation

几何活动轮廓模型是图像分割领域的强有力工具。最近，一种基于成对相似性的图划分活动轮廓(GPAC)模型被提出，并有效应用于均质图像分割。但是，该模型的连接权函数仅与图像光谱相关，使得模型在低对比度模糊图像的应用存在较大局限，同时，成对相似性的计算量大，模型的数值实现效率不甚理想。针对这些问题，该文引入测地核函数定义连接权函数，结合多相水平集，提出了基于局部图划分的多相活动轮廓图像分割模型。自然图像的实验结果证明了该模型的有效性。

Multiphase Joint Segmentation-Registration and Object Tracking for Layered Images

In this paper we propose to jointly segment and register objects of interest in layered images. Layered imaging refers to imageries taken from different perspectives and possibly by different sensors. Registration and segmentation are therefore the two main tasks which contribute to the bottom level, data alignment, of the multisensor data fusion hierarchical structures. Most exploitations of two layered images assumed that scanners are at very high altitudes and that only one transformation ties the two images. Our data are however taken at mid-range and therefore requires segmentation to assist us examining different object regions in a divide-and-conquer fashion. Our approach is a combination of multiphase active contour method with a joint segmentation-registration technique (which we called MPJSR) carried out in a local moving window prior to a global optimization. To further address layered video sequences and tracking objects in frames, we propose a simple adaptation of optical flow calculations along the active contours in a pair of layered image sequences. The experimental results show that the whole integrated algorithm is able to delineate the objects of interest, align them for a pair of layered frames and keep track of the objects over time.