A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape

International Journal of Computer Vision (Impact Factor: 3.81). 04/2007; 72(2):195-215. DOI: 10.1007/s11263-006-8711-1
Source: DBLP


Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90's, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals. We point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.

  • Source
    • "Level set method is an important method that improved its fidelity in many types of images; originally it was used as a numerical technique for tracking interfaces and shapes[1], and has been increasingly applied to image segmentation in the past decade23456. Knowing that this method is able to account with topological changes, and describing multi-component objects, it's still facing a complexity problem though. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Upcoming the past decades, image segmentation became an urgent need due to its significance in the biomedical field. This paper organized segmentation techniques into three, namely: level set, edge-based and a hybrid between these methods. These techniques have been compared, and hybrid method was demonstrated with a high performance capability to detect irregular shapes. This study showed the advantage of deformable technique to segment abnormal cells with Dice similarity value over 80%.
    Full-text · Conference Paper · Oct 2015
  • Source
    • "We discuss the latter approach in more detail in Section 2.2. For methods based on other features, e.g., motion or texture, we refer to [22]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Segmentation is one of the fundamental tasks in computer vision applications. For natural images there exist a vast amount of sophisticated segmentation methods in the literature. However, these standard methods tend to fail in the presence of non-Gaussian image noise, e.g., in biomedical imaging or astronomy. In this paper we propose an adequate variational segmentation model for segmentation of images perturbed by arbitrary noise models without adding a-priori assumptions about the unknown physical noise model. For this, we discuss the prominent minimal surface problem and two different numerical minimization schemes to solve it. The first approach efficiently computes the set of all possible minimal surface solutions for the given data via convex optimization and reduces the segmentation problem to the estimation of a proper threshold. The second approach is based on level set methods and is especially suitable for separation of inhomogeneous image regions. The advantage of this approach is both its simpleness and robustness: the noise in the data does not have to be modeled explicitly since the image intensities are separated using histogram-based thresholding techniques. The proposed model can be interpreted as a generalization of many traditional segmentation methods which implicitly assume a perturbation by additive Gaussian noise. The superiority of this approach over standard methods such as the popular Chan-Vese formulation is demonstrated on synthetic images as well as real application data from biomedical imaging.
    Full-text · Article · Jul 2015 · Pattern Recognition
  • Source
    • "Numerous segmentation techniques exist [13] [14] [15] [16] [17] [18] [19] for producing image series, which can be classified as either boundary or region based. Active contour models, such as snakes [14] [17], are the most popular frameworks for boundary-based methods. "

    Full-text · Dataset · Mar 2015
Show more