Romaric Audigier

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This paper analyzes the robustness issue in three seg-mentation approaches: the iterative relative fuzzy object extraction , the watershed transforms (WT) by image foresting transform and by minimum spanning forest. These methods need input seeds, which can be source of variability in the segmentation result. So, the robustness of these seg-mentation(More)
This paper makes a rereading of two successful image segmentation approaches, the fuzzy connectedness (FC) and the watershed (WS) approaches, by analyzing both by means of the Image Foresting Transform (IFT). This graph-based transform provides a sound framework for analyzing and implementing these methods. This paradigm allows to show the duality existing(More)
There are many watershed transform algorithms in literature but most of them do not exactly correspond to their respective definition. The solution given by such algorithms depends on their implementation. Others fit with their definition which allows multiple solutions. The solution chosen by such algorithms depends on their implementation too. It is the(More)
In this work, a new type of watershed transform is introduced: the Tie-Zone WaterShed (TZWS). This region-based watershed transform does not depend on arbitrary implementation and provides a unique and optimal solution. Indeed, many solutions are sometimes possible when segmenting an image with a watershed algorithm. In this case, the TZWS assigns each(More)
In a recent paper [1], a new type of watershed (WS) transform was introduced: the tie-zone watershed (TZWS). This region-based watershed transform does not depend on arbitrary implementation and provides a unique (and thereby unbiased) optimal solution. Indeed, many optimal solutions are sometimes possible when segmenting an image by WS. The TZWS assigns(More)
This work presents two fast and iterative methods that integrate segmentation by watershed and three-dimensional visualization, while the classical approach is to separate these two processes. The user-aided segmentation is based on iterative watershed, efficiently implemented using the Image Foresting Transform (IFT). The first proposed algorithm consists(More)
In medical imaging, many applications require visualization and/or analysis of three-dimensional (3D) objects (e.g. organs). At same time, object definition often requires considerable user assistance. In this process, objects are usually defined in an iterative way and their visualization during the process is very important to guide the user's actions for(More)