Conference Paper


CNRS, Ecole Nat. Superieure des Telecommun., Paris
DOI: 10.1109/ISBI.2007.356920 Conference: Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on
Source: IEEE Xplore

ABSTRACT This paper introduces a novel methodology for the segmentation of internal brain structures in MRI volumes in the presence of a tumor. The proposed method relies on an initial segmentation of the tumor. Based on the tumor's type, a set of spatial relations between internal structures, remaining stable even in presence of the pathology, is established. Segmentation and recognition of surrounding anatomical structures are based on prior knowledge about their spatial arrangement. Segmentation results on tumors inducing small or large deformations are provided to illustrate the potential of the approach.

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    ABSTRACT: Segmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach.
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    ABSTRACT: Sequential methods for knowledge-based recognition of structures re-quire to define in which order the structures have to be recognized. We propose to address this problem by integrating pre-attention mech-anisms, in the form of a saliency map, in the determination of the or-der. As pre-attention mechanisms extract knowledge from an image without object recognition in ad-vance and do not require any a pri-ori knowledge on the image, they provide useful knowledge for guid-ing object segmentation and recog-nition. Additionally, we make use of generic knowledge of the scene, expressed as spatial relations, since they play a crucial role in model-based image recognition and inter-pretation due to their stability com-pared to many other image appear-ance characteristics. Graphs are well adapted to represent this infor-mation, and finding an order then amounts to find a path in a graph. The proposed algorithms are applied on brain image understanding.

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May 27, 2014