Sparse Representation for Detection of Microcalcification Clusters
Abstract: We present an
approach to detect MCs in mammograms by casting the detection problem as finding
sparse representations of test samples with respect to training samples. The
ground truth training samples of MCs in mammograms are assumed to be known as a
priori. From these samples of the interest object class, a vocabulary of
information-rich object parts is automatically constructed. The sparse
representation is computed by the l1-regularized least square approach using
the interior-point method. The method based on sparse representation expresses
each testing sample as a linear combination of all the training samplesfrom the
vocabulary. The sparse coefficient vector is obtained by l1-regularized least
square through learning. MCs detectionis achieved by defining
discriminatefunctions from the sparse coefficient vector for each category. To
investigate its performance, the proposed method is applied to DDSM datasets
and compared with support vector machines (SVMs) and twin support vector
machines (TWSVMs). The experimental results have shown that the performance of
the proposed method is comparable with or better than those methods. In
addition, the proposed method is more efficient than SVMs and TWSVMs based
methods as it has no need of model selection and parameter optimization.
Author: Xinsheng Zhang, Minghu
Wang, Ji Ma Ji Ma
Journal Code: jptkomputergg120081
