State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities

Abstract

Segmentation of medical images is an important step in various applicationssuch as visualization, quantitative analysis and image-guided surgery. Numerous segmentation methods have been developed in the past two decades for extraction of organ contours on medical images. Low-level segmentation methods, such as pixel-based clustering, region growing, and filter-based edge detection, require additional pre-processing and post-processing as well as considerable amounts of expert intervention or information of the objects of interest. Furthermore, the subsequent analysis of segmented objects is hampered by the primitive, pixel or voxel level representations from those region-based segmentation [1].Deformable models, on the other hand, provide an explicit representation of the boundary and the shape of the object. They combine several desirable features such as inherent connectivity and smoothness, which counteract noise and boundary irregularities, as well as the ability to incorporate knowledge about the object of interest [2,3,4]. However, parametric deformable models have two main limitations. First, in situations where the initial model and desired object boundary differ greatly in size and shape, the model must be reparameterized dynamically to faithfully recover the object boundary. The second limitation 1 Columbia University, New York, NY, USA 48Angelini, Jin, and Laine is that it has difficulty dealing with topological adaptation such as splitting or merging model parts, a useful property for recovering either multiple objects or objects with unknown topology. This difficulty is caused by the fact that a new parameterization must be constructed whenever topology change occurs, which requires sophisticated schemes [5,6]. Level set deformable models [7,8], also referred to as geometric deformable models, provide an elegant solution to address the primary limitations of parametric deformable models. These methods have drawn a great deal of attention since their introduction in 1988.Advantages of the contour implicit formulation of the deformable model over parametric formulation include: (1) no parameterization of the contour, (2) topological flexibility, (3) good numerical stability, (4) straightforward extension of the 2D formulation to n-D. Recent reviews on the subject include papers from Suri [9,10].In this chapter we give a general overview of the level set segmentation methods with emphasis on new frameworks recently introduced in the context of medical imaging problems. We then introduce novel approaches that aim at combining segmentation and registration in a level set formulation. Finally, we review a selective set of clinical works with detailed validation of the level set methods for several clinical applications. Level Set Methods for SegmentationA recent paper from Montagnat, Delingette, and Ayache [11] reviews the general family of deformable models and surfaces with a classification based on their representation. This classification has been reproduced to some extent in Level Set FrameworkSegmen...

Medical Image Segmentation Using Artificial Neural Networks

“…Finally, Gacsádi and Szolgay (2010) proposed a new variational computing based medical segmentation method using CNN. Over the last decade, variational method and partial equations have been widely used for medical image segmentation in algorithms such as level-set (Angelini et al, 2005). This method needs a multi-layered CNN to solve an optimization problem to produce segmentation results.…”

Left-ventricular epi- and endocardium extraction from 3D ultrasound images using an automatically constructed 3D ASM

“…In fact, the suboptimal quality forced many studies to reject up to one-third of the data (Nikitin et al 2006;Bellenger et al 2000). An extensive survey of traditional approaches to US segmentation can be found in Noble and Boukerroui (2006), Frangi et al (2005), Lelieveldt et al (2006), Angelini, Homma et al (2005) and Angelini, Jin et al (2005). The classifications of approaches to modelling cardiac geometry can be found in Montagnat and Delingette (2001) and Frangi et al (2005).…”

Effect of Number of Coupled Structures on the Segmentation of Brain Structures

“…The parametric methods evolve in the Lagrangian framework while the geometric methods evolve in the Eulerian framework. In the definition of the energy function, earlier methods use boundary information of the desired structures [4][5][6]. Later methods use regional information such as intensity histogram (parametric and nonparametric, offline or online) or regional variance of the image intensities [7][8][9].…”