Snakes (Active Contour Models) are powerful model-based image
segmentation tools. Although researchers have proven them especially useful in medical image analysis over the past decade, Snakes have remained primarily in the academic world and they have not become widely used in clinical practice or widely available in commercial packages. A number of confusing and specialized variants exist and there has been no standard open-source implementation available. To address this problem, we present a Java Extensible Snakes System (JESS) that is general, portable, and extensible. The system uses Java Swing classes to allow for the rapid development of custom graphical user interfaces (GUI's). It also incorporates the Java Advanced Imaging (JAI) class library, which provide custom image preprocessing, image display and general image I/O. The Snakes algorithm itself is written in a hierarchical fashion, consisting of a general Snake class and several subclasses that span the main variants of Snakes including a new, powerful, robust subdivision-curve Snake. These subclasses can be easily and quickly extended and customized for any specific segmentation and analysis task. We demonstrate the utility of these classes for segmenting various anatomical structures from 2D medical images. We also demonstrate the effectiveness of JESS by using it to rapidly build a prototype semi-automatic sperm analysis system. The JESS software will be made publicly available in early 2005.
KEYWORDS: Lab on a chip, Principal component analysis, Shape analysis, Medical imaging, Image segmentation, Statistical modeling, Statistical analysis, Data modeling, Brain, Magnetic resonance imaging
Powerful, flexible shape models of anatomical structures are required for robust, automatic analysis of medical images. In this paper we investigate a physics-based shape representation and deformation method in an effort to meet these requirements. Using a medial-based spring-mass mesh model, shape deformations are produced via the application of external forces or internal spring actuation. The range of deformations includes bulging, stretching, bending, and tapering at different locations, scales, and with varying amplitudes. Springs are actuated either by applying deformation operators or by activating statistical modes of variation obtained via a hierarchical regional principal component analysis. We demonstrate results on both synthetic data and on a spring-mass model of the corpus callosum, obtained from 2D mid-sagittal brain Magnetic Resonance (MR) Images.
KEYWORDS: 3D modeling, Data modeling, Motion models, 3D image processing, Image segmentation, 3D image reconstruction, Heart, Finite element methods, Biomedical optics, 3D metrology
This paper presents a physics-based approach to 3D image segmentation using a 3D elastically deformable surface model. This deformable 'balloon' is a dynamic model and its deformation is governed by the laws of nonrigid motion. The formulation of the motion equations includes a strain energy, simulated forces, and other physical quantities. The strain energy stems from a thin-plate under tension spline and the deformation results from the action of internal forces (which describe continuity constraints) and external forces (which describe data compatibility constraints). We employ the finite element method to discretize the deformable balloon model into a set of connected element domains. The finite element method provides an analytic surface representation. Furthermore, we use a finite element with nodal variables which reflect the derivative terms found in the thin-plate under tension energy expression. That is, the nodal variables include not only the nodal positions, but all of the first and second order partial derivatives of the surface as well. This information can be used to compute the volume, shape, and motion properties of the reconstructed biological structures. To demonstrate the usefulness of our 3D segmentation technique and demonstrate the dynamic properties of our model, we apply it to dynamic 3D CT images of a canine heart to reconstruct the left ventricle and track its motion over time.
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