Alberto Bartesaghi

Short note: O(N) implementation of the fast marching algorithm

In this note we present an implementation of the fast marching algorithm for solving Eikonal equations that in practice reduces the original run-time from O(NlogN) to linear. This lower run-time cost is obtained while keeping an error bound of the same order of magnitude as the original algorithm. This improvement is achieved introducing the straight forward untidy priority queue, obtained via a quantization of the priorities in the marching computation. We present the underlying framework, estimations on the error, and examples showing the usefulness of the proposed approach.

An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms

Electron tomography allows for the determination of the three-dimensional structures of cells and tissues at resolutions significantly higher than that which is possible with optical microscopy. Electron tomograms contain, in principle, vast amounts of information on the locations and architectures of large numbers of subcellular assemblies and organelles. The development of reliable quantitative approaches for the analysis of features in tomograms is an important problem, and a challenging prospect due to the low signal-to-noise ratios that are inherent to biological electron microscopic images. This is, in part, a consequence of the tremendous complexity of biological specimens. We report on a new method for the automated segmentation of HIV particles and selected cellular compartments in electron tomograms recorded from fixed, plastic-embedded sections derived from HIV-infected human macrophages. Individual features in the tomogram are segmented using a novel robust algorithm that finds their boundaries as global minimal surfaces in a metric space defined by image features. The optimization is carried out in a transformed spherical domain with the center an interior point of the particle of interest, providing a proper setting for the fast and accurate minimization of the segmentation energy. This method provides tools for the semi-automated detection and statistical evaluation of HIV particles at different stages of assembly in the cells and presents opportunities for correlation with biochemical markers of HIV infection. The segmentation algorithm developed here forms the basis of the automated analysis of electron tomograms and will be especially useful given the rapid increases in the rate of data acquisition. It could also enable studies of much larger data sets, such as those which might be obtained from the tomographic analysis of HIV-infected cells from studies of large populations.

Three-dimensional shape rendering from multiple images

A paradigm for automatic three-dimensional shape and geometry rendering from multiple images is introduced in this paper. In particular, non-photorealistic rendering (NPR) techniques in the style of pen-and-ink illustrations are addressed, while the underlying presented ideas can be used in other modalities, such as halftoning, as well. Existing NPR approaches can be categorized in two groups depending on the type of input they use: image based and object based. Using multiple images as input to the NPR scheme, we propose a novel hybrid model that simultaneously uses information from the image and object domains. The benefit not only comes from combining the features of each approach, it also minimizes the need for manual or user assisted tasks in extracting scene features and geometry, as employed in virtually all state-of-the-art NPR approaches. As particular examples we use input images from binocular stereo and multiple-light photometric stereo systems. From the image domain we extract the tonal information to be mimicked by the NPR synthesis algorithm, and from the object domain we extract the geometry, mainly principal directions, obtained from the image set without explicitly using 3D models, to convey shape to the drawings. We describe a particular implementation of such an hybrid system and present a number of automatically generated pen-and-ink style drawings. This work then shows how to use and extend well-developed techniques in computer vision to address fundamental problems in shape representation and rendering.

Tracking of moving objects under severe and total occlusions

We present an algorithm for tracking moving objects using intrinsic minimal surfaces which handles particularly well the presence of severe and total occlusions even in the presence of weak object boundaries. We adopt an edge based approach and find the segmentation as a minimal surface in 3D space-time, the metric being dictated by the image gradient. Object boundaries are represented implicitly as the level set of a higher dimensional function, and no particular object model is assumed. We also avoid explicit estimation of a dynamic model since the problem is regarded as one of static energy minimization. A set of interior points provided by the user is used to constrain the optimization, which basically corresponds to selecting the object of interest within the video sequence. The constraints are such that they restrict the resulting surface to be star-shaped in the 3D spatio-temporal space. We present some challenging examples that show the robustness of the technique.

A new approach for 3D segmentation of cellular tomograms obtained using three-dimensional electron microscopy

Electron tomography allows determination of the three-dimensional structures of cells and tissues at resolutions significantly higher than is possible with optical microscopy. Electron tomograms contain, in principle, vast amounts of information on the locations and architectures of large numbers of subcellular assemblies and organelles. The development of reliable quantitative approaches for interpretation of features in tomograms, is an important problem, but is a challenging prospect because of the low signal-to-noise ratios that are inherent to biological electron microscopic images. As a first step in this direction, we report methods for the automated statistical analysis of HIV particles and selected cellular compartments in electron tomograms recorded from fixed, plastic-embedded sections derived from HIV-infected human macrophages. Individual features in the tomogram are segmented using a novel, robust algorithm that finds their boundaries as global minimal surfaces in a metric space defined by image features. Our expectation is that such methods will provide tools for semi-automated detection and statistical evaluation of HIV particles at different stages of assembly in the cells, and present opportunities for correlation with biochemical markers of HIV infection.

Nonphotorealistic rendering from multiple images

A new paradigm for automatic nonphotorealistic rendering (NPR) is introduced in this paper. Existing NPR approaches can be categorized in two groups depending on the type of input they use: image based and object based. Using multiple images as input to the NPR scheme, we propose a novel hybrid model that simultaneously uses information from the image and object domains. The benefit not only comes from combining the features of each approach, but most important, it minimizes the need for manual or user assisted tasks in extracting scene features and geometry, as employed in virtually all state-of-the-art NPR approaches. We describe a particular implementation of such an hybrid system and present a number of automatically generated pen-and-ink style drawings. This work then shows how to use and extend well developed techniques in computer vision to address fundamental problems in image representation and rendering.

Segmentation of anatomical structure from DT-MRI

A general framework for the automatic segmentation of anatomical structures from diffusion tensor MRI is presented here. We adopt an energy based approach to segmentation assuming a piecewise-smooth image model that allows tensors to change orientation inside bundles, complemented by adequate modeling of image statistics. Energy minimization is carried out using a greedy region-growing algorithm that is both efficient and robust. Although the framework is general and any tensor metric is supported, we use a simplified tensor representation that adapts well to the DTI setting and further improves computational performance. Segmentation results are generated automatically from a single seed point as illustrated on several real and synthetic datasets