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1.
  • Gal, Yaniv, et al. (author)
  • Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images
  • 2007
  • In: Proc. 2007 Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA). - 0769530672 ; , s. 124-129
  • Conference paper (peer-reviewed)abstract
    • We present a novel method for the segmentation of enhancing breast tissue, suspicious of malignancy, in dynamic contrast-enhanced (DCE) MR images. The method is based on seeded region growing and merging using criteria based on both the original image intensity values and the fitted parameters of a novel empiric parametric model of contrast enhancement. We present the results of the application of the method to DCE-MRI data sets originating from breast MRI examinations of 24 subjects (10 cases of benign and 14 cases of malignant enhancement). The results show that the segmentation method has 100% sensitivity for the detection of suspicious regions independently identified by a radiologist. The results suggest that the method has potential both as a tool to assist the clinician with the task of locating suspicious tissue and as input to a computer assisted diagnostic system for generating quantitative features for automatic classification of suspicious tissue.
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2.
  • Viksten, Fredrik, et al. (author)
  • A Geometry-Based Local Descriptor for Range Data
  • 2007
  • In: Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications. - : ACM. - 0769530672 ; , s. 210-217
  • Conference paper (peer-reviewed)abstract
    • We present a novel local descriptor for range data that can describe one or more planes or lines in a local region. It is possible to recover the geometry of the described local region and extract the size, position and orientation of each local plane or line-like structure from the descriptor. This gives the descriptor a property that other popular local descriptors for range data, such as spin images or point signatures, does not have. The estimation of the descriptor is dependant on estimation of surface normals but does not depend on the specific normal estimation method used. It is shown that is possible to extract how many planar surface regions the descriptor represents and that this could be used as a point-of-interest detector.
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