Publication
A key research area in computer vision is image segmentation. Image segmentation aims at extracting objects of interest in images or video sequences. These objects contain relevant information for a given application. For example, a video surveillance application generally requires to extract moving objects (vehicles, persons or animals) from a sequence of images in order to check that their path stays conformed to the regulation rules set for the observed scene. Image segmentation is not an easy task. In many applications, the contours of the objects of interest are difficult to delineate, even manually. The problems linked to segmentation are often due to low contrast, fuzzy contours or too similar intensities with adjacent objects. In some cases, the objects to be extracted have no real contours in the image. This kind of objects is called virtual objects. Virtual objects appear especially in medical applications. To draw them, medical experts usually estimate their position from surrounding objects. The problems related to image segmentation can be greatly simplified with information known in advance on the objects to be extracted (the prior knowledge). A widely used method consists to extract the needed prior knowledge from a reference image often called atlas. The goal of the atlas is to describe the image to be segmented like a map would describe the components of a geographical area. An atlas can contain three types of information on each object being part of the image: an estimation of its position in the image, a description of its shape and texture, and the features of its adjacent objects. The atlas-based segmentation method is rather used when the atlas can characterize a range of images. This method is thus especially adapted to medical images due to the existing consistency between anatomical structures of same type. There exist two types of atlas: the determinist atlas and the statistical atlas. The determinist atlas is an image which has been selected or computed, to be the most representative of an image category to be segmented. This image is called intensity atlas. The contours of the objects of interest (the objects to be extracted in images of the same type) have been traced manually on the intensity atlas, or by using a semi-automatic method. A label is often attributed to each one of these objects in order to differentiate them. In this way, we obtain a labeled version of the atlas called labeled atlas. The statistical atlas is an atlas created from a database of images in order to be the most representative of a certain type of images to be segmented. In this atlas, the position and the features of the objects of interest depend on statistical measures. In this thesis, we are focused on the use of determinist atlases for image segmentation. The segmentation process with a determinist atlas consists to deform the objects delineated in the atlas in order to better align them with their corresponding objects in the image to