Previous clinical research studies consider the existence of differences among normal individual cerebral cortex and show that some features such as cerebral sulci are unique for each individual so that human brains a...Previous clinical research studies consider the existence of differences among normal individual cerebral cortex and show that some features such as cerebral sulci are unique for each individual so that human brains are anatomically different and unique to each individual. In this work, we highlight the idea which consists in using medical data, such as brain MRI images for the purpose of individual identification or verification. In other words, we raise the following question: can one identify individuals using their brain geometry characteristics? Our aim is to validate the feasibility of this new biometrics tool based on human brain characterization. The proposed approach differs from existent biometrics modalities (e.g. fingerprint, hand, etc.) in the sense that brain features cannot be modified by individuals as it is the case when using fake fingerprints, or fake hands. In this work, we consider volumetric Magnetic Resonance Images (MRI) from which brain shapes are extracted using a 3D level-sets segmentation approach. Afterwards, geometrical descriptors are extracted from the 3D brain volume and from a projected version which provide specific features such as the isoperimetric ratio, the cortical surface curvature and the Gyrification index. Evaluations performed on a set of MRI images obtained from (MeDEISA) database show that it is possible to distinguish between individuals using their brain characteristics. Preliminary results are particularly encouraging.展开更多
In this paper we present a novel approach for brain surfacec characterization based on convexity and concavity analysis of cortical surface mesh. Initially, volumetric Magnetic Resonance Images (MRI) data is processed...In this paper we present a novel approach for brain surfacec characterization based on convexity and concavity analysis of cortical surface mesh. Initially, volumetric Magnetic Resonance Images (MRI) data is processed to generate a discrete representation of cortical surface using low-level segmentation tools and Level-Sets method. Afterward, pipeline procedure for brain characterization/labeling is developed. The first characterization method is based on discrete curvature classification. This is consists on estimating curvature information at each vertex in the cortical surface mesh. The second method is based on transforming the brain surface mesh into Digital Elevation Model (DEM), where each vertex is designed by its space coordinates and geometric measures related to a reference surface. In other word, it consists on analyzing the cortical surface as a topological map or an elevation map where the ridge or crest lines represent cortical gyri and valley lines represents sulci. The experimental results have shown the importance of these characterization methods for the detection of significant details related to the cortical surface.展开更多
文摘Previous clinical research studies consider the existence of differences among normal individual cerebral cortex and show that some features such as cerebral sulci are unique for each individual so that human brains are anatomically different and unique to each individual. In this work, we highlight the idea which consists in using medical data, such as brain MRI images for the purpose of individual identification or verification. In other words, we raise the following question: can one identify individuals using their brain geometry characteristics? Our aim is to validate the feasibility of this new biometrics tool based on human brain characterization. The proposed approach differs from existent biometrics modalities (e.g. fingerprint, hand, etc.) in the sense that brain features cannot be modified by individuals as it is the case when using fake fingerprints, or fake hands. In this work, we consider volumetric Magnetic Resonance Images (MRI) from which brain shapes are extracted using a 3D level-sets segmentation approach. Afterwards, geometrical descriptors are extracted from the 3D brain volume and from a projected version which provide specific features such as the isoperimetric ratio, the cortical surface curvature and the Gyrification index. Evaluations performed on a set of MRI images obtained from (MeDEISA) database show that it is possible to distinguish between individuals using their brain characteristics. Preliminary results are particularly encouraging.
文摘In this paper we present a novel approach for brain surfacec characterization based on convexity and concavity analysis of cortical surface mesh. Initially, volumetric Magnetic Resonance Images (MRI) data is processed to generate a discrete representation of cortical surface using low-level segmentation tools and Level-Sets method. Afterward, pipeline procedure for brain characterization/labeling is developed. The first characterization method is based on discrete curvature classification. This is consists on estimating curvature information at each vertex in the cortical surface mesh. The second method is based on transforming the brain surface mesh into Digital Elevation Model (DEM), where each vertex is designed by its space coordinates and geometric measures related to a reference surface. In other word, it consists on analyzing the cortical surface as a topological map or an elevation map where the ridge or crest lines represent cortical gyri and valley lines represents sulci. The experimental results have shown the importance of these characterization methods for the detection of significant details related to the cortical surface.
