Catherine Garbay

Image Structure Representation and Processing: A Discussion of Some Segmentation Methods in Cytology

Image processing methods (segmentation) are presented in connection with a modeling of image structure. An image is represented as a set of primitives, characterized by their type, abstraction level, and a list of attributes. Entities (regions for example) are then described as a subset of primitives obeying particular rules. Image segmentation methods are discussed, according to the associated image modeling level. Their potential efficacity is compared, when applied to cytologic image analysis.

An Iterative Segmentation Method Based on a Contextual Color and Shape Criterion

An iterative segmentation method is presented and illustrated on specific examples. Full control of each iteration step is obtained by combining local and global properties according to a model of the image structure. A consistent convergence criterion is derived from additional image structure properties and a test is proposed to evaluate adequacy of segmentation.

Distributed Local MRF Models for Tissue and Structure Brain Segmentation

Accurate tissue and structure segmentation of magnetic resonance (MR) brain scans is critical in several applications. In most approaches this task is handled through two sequential steps. We propose to carry out cooperatively both tissue and subcortical structure segmentation by distributing a set of local and cooperative Markov random field (MRF) models. Tissue segmentation is performed by partitioning the volume into subvolumes where local MRFs are estimated in cooperation with their neighbors to ensure consistency. Local estimation fits precisely to the local intensity distribution and thus handles nonuniformity of intensity without any bias field modelization. Similarly, subcortical structure segmentation is performed via local MRF models that integrate localization constraints provided by a priori fuzzy description of brain anatomy. Subcortical structure segmentation is not reduced to a subsequent processing step but joined with tissue segmentation: the two procedures cooperate to gradually and conjointly improve model accuracy. We propose a framework to implement this distributed modeling integrating cooperation, coordination, and local model checking in an efficient way. Its evaluation was performed using both phantoms and real 3 T brain scans, showing good results and in particular robustness to nonuniformity and noise with a low computational cost. This original combination of local MRF models, including anatomical knowledge, appears as a powerful and promising approach for MR brain scan segmentation.

Multimodal MRI segmentation of ischemic stroke lesions

The problem addressed in this paper is the automatic segmentation of stroke lesions on MR multi-sequences. Lesions enhance differently depending on the MR modality and there is an obvious gain in trying to account for various sources of information in a single procedure. To this aim, we propose a multimodal Markov random field model which includes all MR modalities simultaneously. The results of the multimodal method proposed are compared with those obtained with a mono-dimensional segmentation applied on each MRI sequence separately. We constructed an Atlas of blood supply territories to help clinicians in the determination of stroke subtypes and potential functional deficit.

A cooperative framework for segmentation of MRI brain scans

Automatic segmentation of MRI brain scans is a complex task for two main reasons: the large variability of the human brain anatomy, which limits the use of general knowledge and, inherent to MRI acquisition, the artifacts present in the images that are difficult to process. To tackle these difficulties, we propose to mix, in a cooperative framework, several types of information and knowledge provided and used by complementary individual systems: presently, a multi-agent system, a deformable model and an edge detector. The outcome is a cooperative segmentation performed by a set of region and edge agents constrained automatically and dynamically by both, the specific gray levels in the considered image, statistical models of the brain structures and general knowledge about MRI brain scans. Interactions between the individual systems follow three modes of cooperation: integrative, augmentative and confrontational cooperation, combined during the three steps of the segmentation process namely, the specialization of the seeded-region-growing agents, the fusion of heterogeneous information and the retroaction over slices. The described cooperative framework allows the dynamic adaptation of the segmentation process to the own characteristics of each MRI brain scan. Its evaluation using realistic brain phantoms is reported.

Automated segmentation of human brain MR images using a multi-agent approach

Image interpretation consists in finding a correspondence between radiometric information and symbolic labeling with respect to specific spatial constraints. It is intrinsically a distributed process in terms of goals to be reached, zones in the image to be processed and methods to be applied. To cope the the difficulty inherent in this process, several information processing steps are required to gradually extract information. In this paper we advocate the use of situated cooperative agents as a framework for managing such steps. Dedicated agent behaviors are dynamically adapted depending on their position in the image, of their topographic relationships and of the radiometric information available. The information collected by the agents is gathered, shared via qualitative maps, or used as soon as available by acquaintances. Incremental refinement of interpretation is obtained through a coarse to fine strategy. Our work is essentially focused on radiometry-based tissue interpretation where knowledge is introduced or extracted at several levels to estimate models for tissue-intensity distribution and to cope with noise, intensity non-uniformity and partial volume effect. Several experiments on phantom and real images were performed. A complete volume can be segmented in less than 5 min with about 0.84% accuracy of the segmented reference. Comparison with other techniques demonstrates the potential interest of our approach for magnetic resonance imaging (MRI) brain scan interpretation.

Learning recurrent behaviors from heterogeneous multivariate time-series

OBJECTIVE For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. METHODS The proposed approach allows for mixed time-series - containing both pattern and non-pattern data - such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. RESULTS We present the results of our approach on synthetic data generated in the context of monitoring a person at home, as well as early results on few real sequences. The purpose is to build a behavioral profile of a person in their daily activities by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors. CONCLUSIONS The results are very promising. They also highlight the difficulty of tuning the parameters of the method.

Knowledge construction from time series data using a collaborative exploration system

This paper deals with the exploration of biomedical multivariate time series to construct typical parameter evolution or scenarios. This task is known to be difficult: the temporal and multivariate nature of the data at hand and the context-sensitive aspect of data interpretation hamper the formulation of a priori knowledge about the kind of patterns that can be detected as well as their interrelations. This paper proposes a new way to tackle this problem based on a human-computer collaborative approach involving specific annotations. Three grounding principles, namely autonomy, adaptability and emergence, support the co-construction of successive abstraction levels for data interpretation. An agent-based design is proposed to support these principles. Preliminary results in a clinical context are presented to support our proposal. A comparison with two well-known time series exploration tools is furthermore performed.

Distributed Markovian segmentation: Application to MR brain scans

A situated approach to Markovian image segmentation is proposed based on a distributed, decentralized and cooperative strategy for model estimation. According to this approach, the EM-based model estimation is performed locally to cope with spatially varying intensity distributions, as well as non-homogeneities in the appearance of objects. This distributed segmentation is performed under a collaborative and decentralized strategy, to ensure the consistency of segmentation over neighboring zones, and the robustness of model estimation in front of small samples. Specific coordination mechanisms are required to guarantee the proper management of the corresponding processing, which are implemented in the framework of a reactive agent-based architecture. The approach has been experimented on phantoms and real 1.5T MR brain scans. The reported evaluation results demonstrate that this approach is particularly appropriate in front of complex and spatially variable image models.

A society of goal-oriented agents for the analysis of living cells

This paper presents a new model for the segmentation and analysis of living cells. A multi-agent model has been developed for this application. It is based on a generic agent model, which is composed of different behaviors: perception, interaction and reproduction. The agent is further specialized to accomplish a specific goal. Different goals are defined from the different components of the cell images. The specialization specifies the parameters of the behaviors for the achievement of the agents goal. From these goal-oriented agents, a society is defined, and it evolves dynamically as the agents are created and deleted. An internal manager is integrated in the agent to control the behaviors execution. It makes use of an event-driven scheme to manage the behavior priorities. The present design is mainly oriented toward image segmentation, however, it includes some features on tracking and motion analysis.