Olivier Pierre Nempont

A New Fuzzy Connectivity Measure for Fuzzy Sets

Fuzzy set theory constitutes a powerful representation framework that can lead to more robustness in problems such as image segmentation and recognition. This robustness results to some extent from the partial recovery of the continuity that is lost during digitization. In this paper we deal with connectivity measures on fuzzy sets. We show that usual fuzzy connectivity definitions have some drawbacks, and we propose a new definition that exhibits better properties, in particular in terms of continuity. This definition leads to a nested family of hyperconnections associated with a tolerance parameter. We show that corresponding connected components can be efficiently extracted using simple operations on a max-tree representation. Then we define attribute openings based on crisp or fuzzy criteria. We illustrate a potential use of these filters in a brain segmentation and recognition process.

A constraint propagation approach to structural model based image segmentation and recognition

The interpretation of complex scenes in images requires knowledge regarding the objects in the scene and their spatial arrangement. We propose a method for simultaneously segmenting and recognizing objects in images, that is based on a structural representation of the scene and a constraint propagation method. The structural model is a graph representing the objects in the scene, their appearance and their spatial relations, represented by fuzzy models. The proposed solver is a novel global method that assigns spatial regions to the objects according to the relations in the structural model. We propose to progressively reduce the solution domain by excluding assignments that are inconsistent with a constraint network derived from the structural model. The final segmentation of each object is then performed as a minimal surface extraction. The contributions of this paper are illustrated through the example of brain structure recognition in magnetic resonance images.

Combining radiometric and spatial structural information in a new metric for minimal surface segmentation

Segmentation of anatomical structures via minimal surface extraction using gradient-based metrics is a popular approach, but exhibits some limits in the case of weak or missing contour information. We propose a new framework to define metrics, robust to missing image information. Given an object of interest we combine gray-level information and knowledge about the spatial organization of cerebral structures, into a fuzzy set which is guaranteed to include the objects boundaries. From this set we derive a metric which is used in a minimal surface segmentation framework. We show how this metric leads to improved segmentation of subcortical gray matter structures. Quantitative results on the segmentation of the caudate nucleus in T1 MRI are reported on 18 normal subjects and 6 pathological cases.

A new fuzzy connectivity class application to structural recognition in images

Fuzzy sets theory constitutes a poweful tool, that can lead to more robustness in problems such as image segmentation and recognition. This robustness results to some extent from the partial recovery of the continuity that is lost during digitization. Here we deal with fuzzy connectivity notions. We show that usual fuzzy connectivity definitions have some drawbacks, and we propose a new definition, based on the notion of hyperconnection, that exhibits better properties, in particular in terms of continuity. We illustrate the potential use of this definition in a recognition procedure based on connected filters. A max-tree representation is also used, in order to deal efficiently with the proposed connectivity.

Abdominal arteries recognition in x-ray using a structural model

The automatic recognition of vascular trees is a challenging task, required for roadmapping or advanced visualization. For instance, during an endovascular aneurysm repair (EVAR), the recognition of abdominal arteries in angiograms can be used to select the appropriate stent graft. This choice is based on a reduced set of arteries (aorta, renal arteries, iliac arteries) whose relative positions are quite stable. We propose in this article a recognition process based on a structural model. The centerlines of the target vessels are represented by a set of control points whose relative positions are constrained. To find their position in an angiogram, we enhance the target vessels and extract a set of possible positions for each control point. Then, a constraint propagation algorithm based on the model prunes those sets of candidates, removing inconsistent ones. We present preliminary results on 5 cases, illustrating the potential of this approach and especially its ability to handle the high variability of the target vessels.