Tim McInerney

Deformable models in medical image analysis: a survey.

This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics and approximation theory. They have proven to be effective in segmenting, matching and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching and motion tracking.

Topologically adaptable snakes

The paper presents a typologically adaptable snakes model for image segmentation and object representation. The model is embedded in the framework of domain subdivision using simplicial decomposition. This framework extends the geometric and topological adaptability of snakes while retaining all of the features of traditional snakes, such as user interaction, and overcoming many of the limitations of traditional snakes. By superposing a simplicial grid over the image domain and using this grid to iteratively reparameterize the deforming snakes model, the model is able to flow into complex shapes, even shapes with significant protrusions or branches, and to dynamically change topology as necessitated by the data. Snakes can be created and can split into multiple parts or seamlessly merge into other snakes. The model can also be easily converted to and from the traditional parametric snakes model representation. We apply a 2D model to various synthetic and real images in order to segment objects with complicated shapes and topologies.<<ETX>>

A finite element model for 3D shape reconstruction and nonrigid motion tracking

The authors present a physics-based approach for recovering the 3-D shape and tracking the motion of nonrigid objects using a 3-D elastically deformable balloon model. The balloon model is based on a thin-plate under tension spline which deforms to fit visual data according to internal forces stemming from the elastic properties of the surface and external forces which are produced from the data. The finite element method is used to represent the model as a continuous surface. A natural finite element is used whose nodal variables comprise the position of the surface plus its first and second partial derivatives, reflecting each of the partial derivatives that occur in the splines strain energy functional. Hence, the model directly estimates all the information needed to measure the differential geometric properties of the fitted surface. The balloon model was applied to the reconstruction of 3-D objects with irregular shape features. Its effectiveness is demonstrated in extracting the left ventricular surface and tracking its nonrigid motion in dynamic computerized tomography volume images.<<ETX>>

Deformable models in medical image analysis

This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size, and shape of these structures. Deformable model are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching, and motion tracking.

A non-self-intersecting adaptive deformable surface for complex boundary extraction from volumetric images

Abstract This paper proposes a non-self-intersecting multiscale deformable surface model with an adaptive remeshing capability. The model is specifically designed to extract the three-dimensional boundaries of topologically simple but geometrically complex anatomical structures, especially those with deep concavities such as the brain, from volumetric medical images. The model successfully addresses three significant problems of conventional deformable models when dealing with such structures-sensitivity to model initialization, difficulties in dealing with severe object concavities, and model self-intersection. The first problem is addressed using a multiscale scheme, which extracts the boundaries of objects in a coarse-to-fine fashion by applying a multiscale deformable surface model to a multiresolution volume image pyramid. The second problem is addressed with adaptive remeshing, which progressively resamples the triangulated deformable surface model both globally and locally, matching its resolution to the levels of the volume image pyramid. Finally, the third problem is solved by including a non-self-intersection force among the customary internal and external forces in a physics-based model formulation. Our deformable surface model is more efficient, much less sensitive to initialization and spurious image features, more proficient in extracting boundary concavities, and not susceptible to self-intersections compared to most other models of its type. This paper presents results of applying our new deformable surface model to the extraction of a spherical surface with concavities from a computer-generated volume image and a brain cortical surface from a real MR volume image.

Extending distributed shared memory to heterogeneous environments

The problems of building a distributed shared memory system on a network of heterogeneous machines are discussed. An existing algorithm (due to K. Li, 1986) that implements distributed shared memory is extended to a heterogeneous environment. An implementation that runs on Sun and DEC Firefly multiprocessor workstations connected by Ethernet is described. Related implementation and performance issues are discussed. On the basis of measurements of the applications ported to the system, it is concluded that heterogeneous distributed shared memory is not only feasible but can also be compared in performance to its homogeneous counterpart.<<ETX>>

Interactive Medical Image Segmentation with United Snakes

Snakes have become a standard image analysis technique with several variants now in common use. We have developed a software package called “United Snakes”. It unifies the most important snake variants, including finite difference, B-spline, and Hermite polynomial snakes, within the framework of a general finite element formulation with a choice of shape functions. Furthermore, we have incorporated into united snakes a recently proposed snake-like technique known as “livewire”, via a method for imposing hard constraints on snakes. Here, we demonstrate that the combination of techniques in united snakes yields generality, accuracy, ease of use, and robustness in several medical image analysis applications, including the segmentation of neuronal dendrites in EM images, dynamic chest image analysis, and the quantification of growth plates.

MEDIAL PROFILES FOR MODELING DEFORMATION AND STATISTICAL ANALYSIS OF SHAPE AND THEIR USE IN MEDICAL IMAGE SEGMENTATION

We present a novel medial-based, multi-scale approach to shape representation and controlled deformation. We use medial-based profiles for shape representation, which follow the geometry of the structure and describe general, intuitive, and independent shape measures (length, orientation, and thickness). Controlled shape deformations (stretch, bend, and bulge) are obtained either as a result of applying deformation operators at certain locations and scales on the medial profiles, or by varying the weights of the main variation modes obtained from a new hierarchical (multi-scale) and regional (multi-location) principal component analysis of the medial profiles. We demonstrate the ability to produce controlled shape deformations on a medial-based representation of the corpus callosum. We show how this control of shape deformations facilitates the design of a layered framework for image segmentation and present results of segmenting the corpus callosum from 2D mid-sagittal magnetic resonance images of the human brain. Furthermore we show how the medial-based representation facilitates hierarchical, deformation-specific statistical shape analysis of segmented corpora callosa.

Physics-Based Shape Deformations for Medical Image Analysis

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.

SketchSnakes: sketch-line initialized Snakes for efficient interactive medical image segmentation.

We present an intuitive, fast and accurate 2D interactive segmentation method that combines a general subdivision-curve Snake possessing powerful editing capabilities, with a novel sketch-line user initialization process, and a pen input device. Using the pen (or a mouse), the Snake is quickly and precisely initialized with a few quick sketch lines drawn across the width of the target object. The smooth contour constructed using these lines is extremely close to the position and shape of the object boundary. This makes the Snakes task of snapping to the object boundary much simpler and hence more likely to succeed in noisy images with minimal user editing. We apply our Snake to the segmentation of several 2D medical images to demonstrate its efficiency, accuracy and robustness. We also compare SketchSnakes to Adobe Photoshops Magnetic Lasso (Adobe Systems Inc., Adobe Photoshop User Guide, 2002) as well as a recent graph-cut based image cutout tool known as Snap (Digital Film Tools LLC, Snap User Guide, 2007) in order to highlight SketchSnakes effectiveness.