Bernard Chalmond

Using hidden scale for salient object detection

This paper describes a method for detecting salient regions in remote-sensed images, based on scale and contrast interaction. We consider the focus on salient structures as the first stage of an object detection/recognition algorithm, where the salient regions are those likely to contain objects of interest. Salient objects are modeled as spatially localized and contrasted structures with any kind of shape or size. Their detection exploits a probabilistic mixture model that takes two series of multiscale features as input, one that is more sensitive to contrast information, and one that is able to select scale. The model combines them to classify each pixel in salient/nonsalient class, giving a binary segmentation of the image. The few parameters are learned with an EM-type algorithm

Isotropic high-resolution three-dimensional confocal micro-rotation imaging for non-adherent living cells.

Recently, micro‐rotation confocal microscopy has enabled the acquisition of a sequence of micro‐rotated images of nonadherent living cells obtained during a partially controlled rotation movement of the cell through the focal plane. Although we are now able to estimate the three‐dimensional position of every optical section with respect to the cell frame, the reconstruction of the cell from the positioned micro‐rotated images remains a last task that this paper addresses. This is not strictly an interpolation problem since a micro‐rotated image is a convoluted two‐dimensional map of a three‐dimensional reality. It is rather a ‘reconstruction from projection’ problem where the term projection is associated to the PSF of the deconvolution process. Micro‐rotation microscopy has a specific difficulty. It does not yield a complete coverage of the volume. In this paper, experiments illustrate the ability of the classical EM algorithm to deconvolve efficiently cell volume despite of the incomplete coverage. This cell reconstruction method is compared to a kernel‐based method of interpolation, which does not take account explicitly the point‐spread‐function (PSF). It is also compared to the standard volume obtained from a conventional z‐stack. Our results suggest that deconvolution of micro‐rotation image series opens some exciting new avenues for further analysis, ultimately laying the way towards establishing an enhanced resolution 3D light microscopy.

Real-time label-free detection of dividing cells by means of lensfree video-microscopy

Abstract. Quantification of cell proliferation and monitoring its kinetics are essential in fields of research such as developmental biology, oncology, etc. Although several proliferation assays exist, monitoring cell proliferation kinetics remains challenging. We present a novel cell proliferation assay based on real-time monitoring of cell culture inside a standard incubator using a lensfree video-microscope, combined with automated detection of single cell divisions over a population of several thousand cells. Since the method is based on direct visualization of dividing cells, it is label-free, continuous, and not sample destructive. Kinetics of cell proliferation can be monitored from a few hours to several days. We compare our method to a standard assay, the EdU proliferation assay, and as proof of principle, we demonstrate concentration-dependent and time-dependent effect of actinomycin D—a cell proliferation inhibitor.

An integrated statistical approach for volume reconstruction from unregistered sequential slices

We address the problem of volume reconstruction from a sequence of cross-sections in the case where the cross-section positions are unknown. This implies performing simultaneous registration and reconstruction. We propose a statistical formulation of the problem leading to an energy minimization algorithm as well as an automatic calibration procedure for the energy parameters. This method has been developed in the context of micro-rotation confocal microscopy. Experiments in this context illustrate the ability of this method to reconstruct efficiently the object of interest.

Video lensfree microscopy of 2D and 3D culture of cells

Innovative imaging methods are continuously developed to investigate the function of biological systems at the microscopic scale. As an alternative to advanced cell microscopy techniques, we are developing lensfree video microscopy that opens new ranges of capabilities, in particular at the mesoscopic level. Lensfree video microscopy allows the observation of a cell culture in an incubator over a very large field of view (24 mm2) for extended periods of time. As a result, a large set of comprehensive data can be gathered with strong statistics, both in space and time. Video lensfree microscopy can capture images of cells cultured in various physical environments. We emphasize on two different case studies: the quantitative analysis of the spontaneous network formation of HUVEC endothelial cells, and by coupling lensfree microscopy with 3D cell culture in the study of epithelial tissue morphogenesis. In summary, we demonstrate that lensfree video microscopy is a powerful tool to conduct cell assays in 2D and 3D culture experiments. The applications are in the realms of fundamental biology, tissue regeneration, drug development and toxicology studies.

Confocal bi-protocol: a new strategy for isotropic 3D live cell imaging.

The conventional approach for microscopic 3D cellular imaging is based on axial through‐stack image series which has some significant limitations such as anisotropic resolution and axial aberration. To overcome these drawbacks, we have recently introduced an alternative approach based on micro‐rotation image series. Unfortunately, this new technique suffers from a huge burden of computation that makes its use quite difficult for current applications. To address these problems we propose a new imaging strategy called bi‐protocol, which consists of coupling micro‐rotation acquisition and conventional z‐stack acquisition. We experimentally prove bi‐protocol 3D reconstruction produces similar quality to that of pure micro‐rotation, but offers the advantage of reduced computation burden because it uses the z‐stack volume to accelerate the registration of the micro‐rotation images.

Coherent 3-D echo detection for ultrasonic imaging

The purpose of the present paper is to present an ultrasonic processing set-up by which three-dimensional (3-D) echo location can be computed more efficiently than by other one-dimensional (1-D) methods. This set-up contains three successive tasks. The first one deals with a model for representing echoes. This model is based on a generic wavelet, which is a cosine function with variable amplitude and phase. To estimate the wavelet, we propose to use a spline representation of its complex envelope in order to reduce amplitude and phase dimension. The second task deals with 1-D detection and is conducted within a Bayesian framework. Using an Ascan decomposition on a family of wavelets resulting from the first task, we propose a specific procedure to carry on constrained least-squares in order to alleviate the bias inherent to this criterion. The third task deals with the spatial regularization of the detected echo location field resulting from the second task. We propose a Bayes-Markov model for removing isolated wrong detections and simultaneously improving, under regularization constraint, the spatial location of the detected echoes. In fact, this model deals with the general problem of nonorganized point approximation. All the proposed techniques are illustrated on real ultrasonic data.

Position of Principal component analysis among Auto-associative composite models

Abstract We introduce Auto-associative composite models, which have shown a good behavior on real data sets, and share important theoretical approximation properties. Their basic principle is to approximate iteratively data by manifolds of increasing dimension. We exhibit a special class of such models: auto-associative additive models. Their use is widespread in Projection pursuit regression. First, we show that Principal component analysis is a linear auto-associative additive model. Then, we show that principal component analysis is the only auto-associative composite model which is additive.

Lensless imaging system to quantify cell proliferation

Owing to its simplicity, lensless imaging system is adept at continuous monitoring of adherent cells inside the incubator. The setup consists of a CMOS sensor with pixel pitch of 2.2 μm and field of view of 24 mm2, LED with a dominating wavelength of 525 nm, along with a pinhole of 150 μm as the source of illumination. The in-line hologram obtained from cells depends on the degree of cell-substrate adhesion. Drastic difference is observed between the holographic patterns of floating and adherent cells. In addition, the well-established fact of reduction of cell-substrate contact during cell division is observed with our system based on corresponding spontaneous transition in the holographic pattern. Here, we demonstrate that by recognizing this specific holographic pattern, number of cells undergoing mitosis in a cell culture with a population of approximately 5000 cells, can be estimated in real-time. The method is assessed on comparison with Edu-based proliferation assay. The approach is straightforward and it eliminates the use of markers to estimate the proliferation rate of a given cell culture. Unlike most proliferation assays, the cells are not harvested enabling continuous monitoring of cell culture.

Micro-rotation imaging deconvolution

Recently, micro-rotation confocal microscopy has enabled the acquisition of a sequence of slices of non adherent living cells obtained during a partially controlled rotation movement of the cell through the focal plane. Although we are now able to estimate the 3D position of every slice with respect to the frame, the reconstruction of the cell from the positioned slices remains a problem that this paper address. In our context, 3D spatially-varying PSF and missing data are the two main particularities of this problem. Experiments illustrate the ability of the classical EM algorithm to deconvolve efficiently cell volume and also to deal with missing data.