We introduce a new paradigm for automatic medical image analysis that adopts concepts from the field of Artificial Life. Our approach prescribes deformable organisms, autonomous agents whose objective is the segmentation and analysis of anatomical structures in medical images. A deformable organism is structured as a ‘muscle’-actuated ‘body’ whose behavior is controlled by a ‘brain’ that is capable of making both reactive and deliberate decisions. This intelligent deformable model possesses an ‘awareness’ of the segmentation process, which emerges from a conflux of perceived sensory data, an internal mental state, memorized knowledge, and a cognitive plan. We develop a class of deformable organisms using a medial representation of body morphology that facilitates a variety of controlled local deformations at multiple spatial scales. Specifically, we demonstrate a deformable ‘worm‘ organism that can overcome noise, incomplete edges, considerable anatomical variation, and occlusion in order to segment and label the corpus callosum in 2D mid-sagittal MR images of the brain. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Since their debut in 1987, snakes (active contour models) have become a standard image analysis technique with several variants now in common use. We present a portable, reusable software package called "United Snakes". The package unites the most popular snake variants, including finite difference, B-spline, and Hermite polynomial snakes within the mathematical framework of a general finite element formulation with a choice of shape functions. The package furthermore incorporates a recently proposed snake-like technique known as "livewire". We integrate snakes and livewire by introducing an effective method for imposing hard constraints on snakes. Our experiments demonstrate that snakes and livewire have complementary strengths and that their union offers a more powerful tool for interactive image analysis, especially for medical imaging applications. United Snakes is implemented in Java as a JavaBean so that it can easily be integrated in end user application systems.

Deformable surface models are an attractive method for segmenting the three-dimensional shapes of complex anatomic structures in volumetric medical images. Despite the success of this approach, several problems remain. In this paper, we propose a multiscale deformable surface model with non-intersection constraint forces that successfully addresses three significant problems-sensitivity to model initialization, difficulties in dealing with severe object concavities, and model self-intersection. The first two problems are addressed by the multiscale scheme, which progressively resamples the triangulated, deformable surface model both globally and locally, matching its resolution to the levels of a volume image pyramid. We address the third problem by including a non-intersection constraint force among the customary internal and external forces in the physics-based formulation. We apply our new deformable surface model to the challenging task of extracting brain cortical surfaces.

Deformable models, which include deformable contours (the popular snakes) and deformable surfaces, are a powerful model-based medical image analysis technique. We develop a new class of deformable models by formulating deformable surfaces in terms of an affine cell image decomposition (ACID). Our approach significantly extends standard deformable surfaces, while retaining their interactivity and other desirable properties. In particular, the ACID induces an efficient reparameterization mechanism that enables parametric deformable surfaces to evolve into complex geometries, even modifying their topology as necessary. We demonstrate that our new ACID-based deformable surfaces, dubbed T-surfaces, can effectively segment complex anatomic structures from medical volume images.

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.>

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 spline's 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.>

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.>