Image and Volume Segmentation by Water Flow

A general framework for image segmentation is presented in this paper, based on the paradigm of water flow. The major water flow attributes like water pressure, surface tension and capillary force are defined in the context of force field generation and make the model adaptable to topological and geometrical changes. A flow-stopping image functional combining edge- and region-based forces is introduced to produce capability for both range and accuracy. The method is assessed qualitatively and quantitatively on synthetic and natural images. It is shown that the new approach can segment objects with complex shapes or weak-contrasted boundaries, and has good immunity to noise. The operator is also extended to 3-D, and is successfully applied to medical volume segmentation

Potential Fields as an External Force and Algorithmic Improvements in Deformable Models

Deformable Models are extensively used as a Pattern Recognition technique. They are curves defined within an image domain that can be moved under the influence of internal and external forces. Some trade-offs of standard deformable models algorithms are the selection of image energy function (external force), the location of initial snake and the attraction of contour points to local energy minima when the snake is being deformed. This paper proposes a new procedure using potential fields as external forces. In addition, standard Deformable Models algorithm has been enhanced with both this new external force and algorithmic improvements. The performance of the presented approach has been successfully proved to extract muscles from Magnetic Resonance Imaging (MRI) sequences of Iberian ham at different maturation stages in order to calculate their volume change. The main conclusions of this paper are the practical viability of potential fields used as external forces, as well as the validation of the algorithmic improvements developed. The feasibility of applying Computer Vision techniques, in conjunction with MRI, for determining automatically the optimal ripening time of the Iberian ham is a practical conclusion reached with the proposed approach

Application of active contours with expert knowledge to heart ventricle segmentation

Automatic heart ventricle segmentation in CT heart images can be an element of system supporting pulmonary embolism diagnosis. To solve that problem in this paper an application of two classical active contour models, snakes and geometric active contours, is proposed. The prepared implementation uses the unified model of those techniques which allows to define forces acting upon a contour only once. The nature of the images causes that the process of force construction requires additional expert knowledge since using only the information visible in the image satisfactory results cannot be obtained

A hybrid deformable model 3-D segmentation algorithm

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 151).by Varouj A. Chitilian.S.B.and M.Eng

Constraint energies for the adaptation of 2d river borderlines to airborne laserscanning data using snakes

The German Authoritative Topographic Cartographic Information System (ATKIS) stores the height and the 2D position of the objects in a dual system. The digital terrain model (DTM), often acquired by airborne laser scanning (ALS), supplies the height information in a regular grid, whereas 2D vector data are provided in the digital landscape model (DLM). However, an increasing number of applications, such as flood risk modelling, require the combined processing and visualization of these two data sets. Due to different kinds of acquisition, processing, and modelling discrepancies exist between the DTM and DLM and thus a simple integration may lead to semantically incorrect 3D objects. For example, rivers may flow uphill. In this paper we propose an algorithm for the adaptation of 2D river borderlines to ALS data by means of snakes. Besides the two basic energy terms of the snake, the internal and image energy, 3D object knowledge is introduced in the constraint energy in order to guarantee the semantic correctness of the rivers in a combined data set. The image energy is based on ALS intensity and height information and derived products. Additionally, features of rivers in the DTM, such as the flow direction or the river profile, are formulated as constraints in order to fulfil the semantic properties of rivers and stabilize the adaptation process. Furthermore, the known concept of twin snakes exploits the width of the river and also supports the procedure. Some results are given to show the applicability of the algorithm

Using building and bridge information for adapting roads to ALS data by means of network snakes

In the German Authoritative Topographic Cartographic Information System (ATKIS), the 2D positions and the heights of objects such as roads are stored separately in the digital landscape model (DLM) and digital terrain model (DTM), which is often acquired by airborne laser scanning (ALS). However, an increasing number of applications require a combined processing and visualization of these two data sets. Due to different kinds of acquisition, processing, and modelling discrepancies exist between the DTM and DLM and thus a simple integration may lead to semantically incorrect 3D objects. For example, roads may be situated on strongly tilted DTM parts and rivers sometimes flow uphill. In this paper we propose an algorithm for the adaptation of 2D road centrelines to ALS data by means of network snakes. Generally, the image energy for the snakes is defined based on ALS intensity and height information and derived products. Additionally, buildings and bridges as strong features in height data are exploited in order to support the road adaptation process. Extracted buildings as priors modified by a distance transform are used to create a force of repulsion for the road vectors integrated in the image energy. In contrast, bridges give strong evidence for the correct road position in the height data. Therefore, the image energy is adapted for the bridge points. For that purpose bridge detection in the DTM is performed starting from an approximate position using template matching. Examples are given which apply the concept of network-snakes with new image energy for the adaptation of road networks to ALS data taking advantage of the prior known topology

Dynamical Algebraic Combinatorics, Asynchronous Cellular Automata, and Toggling Independent Sets

Though iterated maps and dynamical systems are not new to combinatorics, they have enjoyed a renewed prominence over the past decade through the elevation of the subfield that has become known as dynamical algebraic combinatorics. Some of the problems that have gained popularity can also be cast and analyzed as finite asynchronous cellular automata (CA). However, these two fields are fairly separate, and while there are some individuals who work in both, that is the exception rather than the norm. In this article, we will describe our ongoing work on toggling independent sets on graphs. This will be preceded by an overview of how this project arose from new combinatorial problems involving homomesy, toggling, and resonance. Though the techniques that we explore are directly applicable to ECA rule 1, many of them can be generalized to other cellular automata. Moreover, some of the ideas that we borrow from cellular automata can be adapted to problems in dynamical algebraic combinatorics. It is our hope that this article will inspire new problems in both fields and connections between them

An adaptive speed term based on homogeneity for level-set segmentation

We tested on an edge map computed from a local homogeneity measurement, which is a potential replacement for the traditional gradient-based edge map in level-set segmentation. In existing level-set methods, the gradient information is used as a stopping criteria for curve evolution, and also provides the attracting force to the zero level-set from the target boundary. However, in a discrete implementation, the gradient-based term can never fully stop the level-set evolution even for ideal edges, leakage is often unavoidable. Also the effective distance of the attracting force and blurring of edges become a trade-off in choosing the shape and support of the smoothing filter. The proposed homogeneity measurement provides easier and more robust edge estimation, and the possibility of fully stopping the level-set evolution. The homogeneity term decreasing from a homogenous region to the boundary, which dramatically increases the effective distance of the attracting force and also provides additional measurement of the overall approximation to the target boundary. Therefore, it provides a reliable criteria of adaptively changing the advent speed. By using this term, the leakage problem was avoided effectively in most cases compared to traditional level-set methods. The computation of the homogeneity is fast and its extension to the 3D case is straightforward