Abdesslam Benzinou

An immune oriented multi-agent system for biological image processing

Abstract In this article, we present a parallel image processing system based on the concept of reactive agents. Our system lies in the oRis language, which allows to describe finely and simply the agents’ behaviors to detect image features. We also present a method of segmentation using a multi-agent system, and two biological applications made with oRis. The stopping of this multi-agent system is implemented through a technique issued from immunology: the apoptosis.

Use of deformable template for two-dimensional growth ring detection of otoliths by digital image processing:: Application to plaice (Pleuronectes platessa) otoliths

Abstract Today, most computer-assisted age reading software is limited to one-dimensional processing. Automated two-dimensional growth ring detection is closely linked to the ability of image processing algorithms to perceive structure continuity. By using deformable models, based on closed B-splines, it is possible to fit a shape to the image features using maximum or minimum criteria according to the nature of translucent or opaque zones. Otolith edge contours were used as templates that were inflated from the core toward the edge in order to detect the growth zones. Template step sizes were modulated by an a priori growth pattern in order to take into account the decrease in ring size. A reference test image set ( n =102) of plaice otoliths, previously aged by one reader, was processed. For the first age groups, there was a total agreement with reader estimates. Mean agreement with reader estimates for age groups 1–5 averaged 80% but decreased for age groups 6–8. Overall, age was significantly underestimated by an average of −0.89 year.

Growth ring detection on fish otoliths by a graph construction

We present an algorithm for the detection of fish otolith growth rings based on a graph construction method. The identification of growth rings, for age estimation, is routinely achieved in fishery laboratories by human readers. One of the major problem encountered during an automatic image processing is the lack of ring continuity perception. We present an approach to this continuity perception based on the 2D reconstruction of rings from the restoration of the connectivity of nodes detected in polar coordinates. The node connection is based on an a priori knowledge of ring geometry.

Variational 1D signal registration and shape geodesics for shape classification: Application to marine biological archives

When two 1D signals are compared, they must be represented in the same reference system. In most cases, biological signals present a big interindividual variability that should be eliminated in order to compare them properly. This variability can be erased by aligning the signals. A robust variational setting is proposed for 1D signal registration and applied to the computation of shape geodesics for shape classification issues. For validation purposes, experiments are carried out on real signals and shapes issued from marine biological archives which depict a high interindividual variability such that registration-based approaches are of particular interest.

Continuity perception using a multi-agent system an application to growth ring detection on fish otoliths

One of the major problems encountered during an automatic contour detection is the lack of structure continuity perception. Active contours are currently used to represent this continuity, but they point out some problems for contour detection in textured, noisy and low contrast images. We present an approach to perceive continuity based on a 2D reconstruction of closed contours using a multi-agent system. Each agent is provided with sensors on the image which allow it to follow local extrema. The purpose is to detect alternative light and dark concentric structures in an image. In the first version of the method, the closed contour was validated when the agent had found again its initial position after having described a circular path. This condition was not easily satisfied in badly contrasted images. We have added high level information about the shape of the contour to improve the detection. Application to fish otolith growth ring detection is presented in the paper.

A comparative evaluation of segmentation methods for dendritic cells identification from microscopic images

Public health is one of the major concerns at the world level. Toxicology is an extremely challenging issue regarding that toxic substances are harmful to human health. In fact, toxicology studies are indispensable to evaluate the toxic effects on humans. Currently, a new evaluation technique based on the analysis of dendritic cells in vitro has been found by researchers. This analysis that remains purely visual is a tedious process, subjective and time-consuming. Therefore, an assessment tool for the analysis of toxic impact using automatic processing techniques by image analysis can be greatly useful for expert biologists. The foremost aim of this paper is to propose two segmentation approaches of dendritic cells from microscopic images and to present a comparative evaluation of them. The first suggested algorithm is based on automatic thresholding and mathematical morphology, while the second one combines the k-means clustering, thresholding and mathematical morphology based operations. For validation purposes, four performance measures were used to assess the obtained segmentation results with the ground truth images, elaborated by expert. Quantitatively, results show that the two suggested algorithms are efficient in identifying dendritic cells from 26 gray-scale images with a segmentation accuracy of 99.00 % and 99.37%, respectively.

Non-rigid registration of shape sequence images: Applications to biological and medical images

This paper deals with the automation of the analysis of images depicting shape sequences. Here, a robust method for matching shape sequence images is developed. First, the successive shapes are represented by a level-set representation, then the algorithm of registration is carried out on this level-set representation. It considers the levels as elements in a shape space and the corresponding matching is that of the optimal geodesic path. This algorithm of registration is tested on synthetic images and real images issued from biological and medical applications where the intensity-based registration fails.

Automatic Cellular Aggregates Quantification for Toxicology using Statistical Learning

Quantification of haematopoietic clusters is largely used in toxicology. However, visually counting and differentiating aggregates is a very tedious and subjective activity because of the difficulties to evaluate the limits between different types of cell clusters. Proposed here, is an automatic solution with a digital imaging system based on the use of statistical learning techniques. We evaluate the performances of several statistical classifiers (SVMs) with an emphasis on the definition of relevant cluster-related features. Performance demonstration is carried out over a reference test set of several tens of cluster images. System efficiency speaks favorably of the ability of the current approach to routine work

Segmentation of dendritic cells from microscopic images using mathematical morphology

For many years, biologists have been interested in toxicology to assess the effects of contamination on humans. In recent years, researchers have found a new evaluation technique based on the analysis of dendritic cells in vitro. Up to now the analysis conducted on these cells remains purely visual in nature. Therefore, it is subjective and time-consuming because of the different morphological features of the cells. Here, we suggest to use automatic processing techniques by image analysis. The foremost goal of this paper is to suggest an assessment tool for the analysis of immunotoxic effects of food contaminants (mycotoxins) on the immune system using automatic segmentation techniques of microscopic images of dendritic cells. The suggested method is based on automatic thresholding and mathematical morphology. For validation purposes, an experimental study is carried out on 55 microscopic images of dendritic cells visually analyzed by an expert in order to make comparisons or to have a reference segmentation of the cells. Results show that the proposed approach is efficient in identifying dendritic cells with a segmentation accuracy of 95%.

Otolith shape classification for fish stock discrimination

The shape analysis of otoliths, which are calcified structures in the inner ear of teleostean fishes, is known to be particularly relevant to address species identification and stock discrimination. Generally, scientists use classical methodologies of statistical analysis and shape recognition such as Fourier shape descriptors and Principal Component Analysis (PCA). These methods are subject to several limitations mainly to their incapacity to locate irregularities because they are based on global characterization of shape. Recently, more advanced techniques are proposed in this context in order to improve classification accuracies. The first recent method exploits the potential of shape geodesics which rely on local shape features for classification issues. The second one addresses the Best-Basis paradigm which combines the Wavelet Transform, and the potential of statistical analysis in order to fully automate the selection process of efficient features for classification. These methods have been shown to significantly outperform the standard approaches but they are not compared together yet. This study compare these two methods on a real dataset. The comparison is performed on 600 striped red mullet calcified structures collected for the NESPMAN European project. For each method, performances are reported for the classification of samples coming from three geographical zones in the Northwest European seas: the Bay of Biscay, a mixing zone composed of the Celtic Sea and the Western English Channel and a northern zone composed of the Eastern English Channel and the North Sea. Comparison shows that both methods lead to same conclusions.