AUTOMATIC DETECTION OF SMALL SPHERICAL LESIONS USING MULTISCALE APPROACH IN 3D MEDICAL IMAGES

Abstract : Automated detection of small, low level shapes such as circular/spherical objects in images is a challenging computer vision problem. For many applications, especially microbleed detection in Alzheimer's disease, an automatic pre-screening scheme is required to identify potential seeds with high sensitivity and reasonable specificity. A new method is proposed to detect spherical objects in 3D medical images within the multi-scale Laplacian of Gaussian framework. The major contributions are (1) breaking down 3D sphere detection into 1D line profile detection along each coordinate dimension, (2) identifying center of structures by normalizing the line response profile and (3) employing eigenvalues of the Hessian matrix at optimum scale for the center points to determine spherical objects. The method is validated both on simulated data and susceptibility weighted MRI images with ground truth provided by a medical expert. Validation results demonstrate that the current approach has higher performance in terms of sensitivity and specificity and is effective in detecting adjacent microbleeds, with invariance to intensity, orientation, translation and object scale.
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Communication dans un congrès
IEEE ICIP 2013, Sep 2013, Melbourne, France. pp.1, 2013
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Dernière modification le : lundi 13 octobre 2014 - 15:43:25
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  • HAL Id : hal-00860520, version 1

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Amir Fazlollahi, Fabrice Meriaudeau, Victor Villemagne, Christopher Rowe, Patricia Desmond, et al.. AUTOMATIC DETECTION OF SMALL SPHERICAL LESIONS USING MULTISCALE APPROACH IN 3D MEDICAL IMAGES. IEEE ICIP 2013, Sep 2013, Melbourne, France. pp.1, 2013. <hal-00860520>

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