Creating virtual scenes using active contours and global motion estimation

This paper presents an automatic tissue segmentation methodology for High-Resolution Ultrasonic Transmission Tomography (HUTT) imagery of biological organs. This method combines a recent segmentation approach: the L-level set active contours algorithm with unsupervised clustering using the agglomerative hierarchical k-means algorithm. The active contours algorithm has been recently explored as a powerful tool for image segmentation since it automatically decomposes a given image into 2L segment classes by utilizing L level set functions and finding the optimal boundaries of the 2L segment classes so that the pixel feature values of each segment are as homogeneous as possible. Unfortunately, the algorithm is often trapped at local minima due to the intrinsic non-convexity of the cost function, especially for noisy data. To overcome this problem, we introduce a multi-stage multi-resolution analysis that optimizes the active contours at successive resolutions of the image data. The resulting segments are then re-clustered by subsequent agglomerative hierarchical k-means clustering that seeks the optimal clusters yielding the minimum within-cluster distance in the feature space. The preliminary studies reported here indicate that this proposed methodology can enhance the accuracy of soft tissue segmentation and provide fully automatic tissue differentiation without any user intervention except for specifying the number of level set functions L.

Temporal segmentation of a video sequence into different shots is fundamental to a number of video retrieval and analysis applications. Motion estimation has been wildly used in many applications of video processing, since it provides the most essential information for an image sequence. In this paper, we explore the possibility to exploit motion and illumination estimation in a video sequence to detect various types of shot changes. Optical flow is the motion vector computed at each pixel in an image sequence from intensity variation. Traditionally, optical flow computational algorithms were derived from the brightness constancy assumption. In this paper, we employ a generalized optical flow constraint that includes an illumination parameter to model local illumination changes. An iterative optical flow and illumination estimation algorithm is developed in this paper to refine the generalized optical flow constraints step by step, thus leading to a very accurate estimation of the optical flow and illumination parameters. Two robust measures are defined from the mean and standard deviation of the estimated intensity compensation values for all the blocks in the same image. Either of these two measures corresponds significantly to various types of shot changes. We show the usefulness of these two measures through experiments.

This chapter focuses on the division and location of brain deformities such as tumors in magnetic resonance imaging (MRI) through Chan-Vese active contour segmentation. Brain tumor division and identification is a major test in the area of biomedical picture processing. To detect the size and location of the tumor, various techniques are available, but active contour gives accurate knowledge of the region for segmentation. Chan-Vese Active contour method provides independent, robust and more flexible segmentation. In this chapter, firstly we used preprocessing technique in which noise and unused parts of the brain and skull are removed, for this we proposed the skull stripping method. Then, we applied feature extraction to enhance the image intensity and quality, and lastly, used Chan-Vese active contour with a level set image segmentation technique to detect the tumor. The tumor area was calculated after tumor detection.

We describe a new method for simultaneous image denoising and level set-based active contour segmentation using multidimensional features. We consider an image to be a surface embedded in a Riemannian manifold. By defining a metric in the embedded space, which in our case includes multidimensional image features as well as a level set-based active contour model, a minimization problem in the image space can be obtained through the Polyakov action framework. The resulting minimization problem is solved with a dual algorithm for efficiency. Benefits of this new method include the fact that it is independent of any artificial “running” parameters, and experiments using both synthetic and real images show that the method is robust with respect to noise and blurry object boundaries.

A background robust active appearance model using active contour technique

Active contours are adapted to image segmentation by energy minimization. The energies often exhibit local minima, requiring regularization. Such an a priori can be expressed as a shape prior and used in two main ways: (1) a shape prior energy is combined with the segmentation energy into a trade-off between prior compliance and accuracy or (2) the segmentation energy is minimized in the space defined by a parametric shape prior. Methods (1) require the tuning of a data-dependent balance parameter and methods (1) and (2) are often dedicated to a specific prior or contour representation, with the prior and segmentation aspects often meshed together, increasing complexity. A general framework for category (2) is proposed: it is independent of the prior and contour representations and it separates the prior and segmentation aspects. It relies on the relationship shown here between the shape gradient, the prior-induced admissible contour transformations, and the segmentation energy minimization.

Automatic building extraction is an important topic for many applications such as urban planning, disaster management, 3D building modeling and updating GIS databases. Its approaches mainly depend on two data sources: light detection and ranging (LiDAR) point cloud and aerial imagery both of which have advantages and disadvantages of their own. In this study, in order to benefit from the advantages of each data sources, LiDAR and image data combined together. And then, the building boundaries were extracted with the automated active contour algorithm implemented in MATLAB. Active contour algorithm uses initial contour positions to segment an object in the image. Initial contour positions were detected without user interaction by a series of image enhancements, band ratio and morphological operations. Four test areas with varying building and background levels of detail were selected from ISPRS’s benchmark Vaihingen and Istanbul datasets. Vegetation and shadows were removed from all the datasets by band ratio to improve segmentation quality. Subsequently, LiDAR point cloud data was converted to raster format and added to the aerial imagery as an extra band. Resulting merged image and initial contour positions were given to the active contour algorithm to extract building boundaries. In order to compare the contribution of LiDAR to the proposed method, the boundaries of the buildings were extracted from the input image before and after adding LiDAR data to the image as a layer. Finally extracted building boundaries were smoothed by the Awrangjeb (Int J Remote Sen 37(3): 551–579. https://doi.org/10.1080/01431161.2015.1131868 , 2016) boundary regularization algorithm. Correctness (Corr), completeness (Comp) and accuracy (Q) metrics were used to assess accuracy of segmented building boundaries by comparing extracted building boundaries with manually digitized building boundaries. Proposed approach shows the promising results with over 93% correctness, 92% completeness and 89% quality.

The problem of segmentation of a given image using the active contour technique is considered. An abstract calculus to find appropriate speed functions for active contour models in image segmentation or related problems based on variational principles is presented. The speed method from shape sensitivity analysis is used to derive speed functions which correspond to gradient or Newton-type directions for the underlying optimization problem. The Newton-type speed function is found by solving an elliptic problem on the current active contour in every time step. Numerical experiments comparing the classical gradient method with Newton's method are presented.

Multiple sclerosis (MS) one of the chronic Idiopathic disease needs a precise segmentation method to analyze the MS White Matter Lesion (WML) variation with respect the Extended Disability scale (EDSS) value of MS MR brain images. In this work, we use the Active contour segmentation method to segment the MS brain tissue from T2W axial view MS preprocessed MR images. The Active contour segmentation to segment MS affected area using the ITK-SNAP 3D segmentation tool. The segmented MS WML lesion image region to measure the MS lesion affected area volume and calculate the image shape and statistical feature value using Gray Level Co-occurrence Matrix (GLCM). The MS WML volume, shape, and statistical feature are correlated with the different EDSS value of the MS patient data set. The shape, statistical feature parameter, and lesion volume correlation study are very useful to monitoring changes associated with improvement of disability stage from 1–9 category of MS progression. Reproducibility and sensitivity must be enhanced to use the analysis for consecutive monitoring in individuals.

Lack of temporal coherence in video segmentation algorithms often leads to flickering or to discrepancies on the segmentation mask boundaries. Active contour video segmentation algorithms can lead to very smooth segmentation masks when they are defined in three dimensions (two spatial and one temporal). In this paper, we will use an active contour method to attract an active surface toward a non-smoothed segmentation mask boundary. This active surface will produce a new segmentation mask which will be smoother than the first one. To achieve this task, we introduce a new region-based term for active contour segmentation in the variational framework. This term attracts the evolving curve to a reference contour. The energy criterion and the evolution equation are defined in n-dimension and we investigate the particular case of two regions. The use of active surfaces to smooth video segmentation masks appears to be a very powerful tool for video postprocessing. The resulting masks are smoother than the original ones, the discrepancies of the segmentation masks are removed and the flickering on the boundary of the segmentation masks are considerably reduced.

In this paper, we describe fast implementations of optical flow and geometric active contours to reliably track flying vehicles. Given the position of the vehicle at time t-1, optical flow information is used to initially place an active contour in the basin of attraction of a region of interest in a given dynamical image at time t. For real-time tracking, fast convergence of the active contour as well as rapid computation of the optical flow are crucial. In this note, we describe algorithms that make fast tracking possible in this framework using only standard computing platforms.

Computer vision based human detection systems are gaining much significance in modern security and surveillance systems. Recently, advanced camera sensors like Microsoft Kinect came to the scene and such devices are capable of detecting both color and depth details from a captured scene. This article proposes an intelligent human segmentation system by analyzing both color and depth details retrieved by an RGB-Depth camera. Here the depth data is analyzed first, to create an initial segmentation of human factors from the scene. Since depth analysis have restrictions in identifying accurate edge details, post processing steps are used for refurbishing the missed human portions in the frame. The hair regions are first restored first using Chan-vase active contour detection and the subset of foreground pixels thus obtained after this coarse level segmentation are used as seeds to segment the rest of the image frame using Grow-cut segmentation. An iterative cellular automation is employed here to classify the foreground human region. This final segmentation mask thus generated after Grow-cut segmentation can be used for segmenting human region from the original image frame. Experimental results show that the segmentation is helpful for analyzing the human factors in each frame and can be used for further human tracking or activity recognition.

Cellular neural networks and active contours: a tool for image segmentation

In the current study two various segmentation methods have been implemented sequentially in computer-aided approach to the assessment of skeletal maturity, where correct location of borders of anatomical structures is a crucial step. The first segmentation stage, based on the Gibbs random fields technique, correctly segments out a bony structure and roughly outlines the edges of cartilage while the second one using the active contour strategy, smoothes them and prepares for the feature extraction stage. A synthetic region of interest has been designed to test and adjust weights of the snake energy functional. These weights have afterwards been applied to a real image data. In comparison to our previous works we observe a significant improvement of boundary location mainly when cartilage is included in the epiphyses.

Ultrasound imaging is an effective approach for diagnosing breast cancer, but it is highly operator-dependent. Recent advances in computer-aided diagnosis have suggested that it can assist physicians in diagnosis. Definition of the region of interest before computer analysis is still needed. Since manual outlining of the tumor contour is tedious and time-consuming for a physician, developing an automatic segmentation method is important for clinical application. The present paper represents a novel method to segment breast ultrasound images. It utilizes a combination of region-based active contour and neutrosophic theory to overcome the natural properties of ultrasound images including speckle noise and tissue-related textures. First, due to inherent speckle noise and low contrast of these images, we have utilized a non-local means filter and fuzzy logic method for denoising and image enhancement, respectively. This paper presents an improved weighted region-scalable active contour to segment breast ultrasound images using a new feature derived from neutrosophic theory. This method has been applied to 36 breast ultrasound images. It generates true-positive and false-positive results, and similarity of 95%, 6%, and 90%, respectively. The purposed method indicates clear advantages over other conventional methods of active contour segmentation, i.e., region-scalable fitting energy and weighted region-scalable fitting energy.