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In this paper, a sparse representation based classification method is explored. For each patient, four volumetric data items including three volumes of diffusion weighted imaging and a computed asymmetry map are employed to extract patch features which are then fed to dictionary learning and classification based on sparse representation. Elastic net is adopted to replace the traditional\n                    <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                      <mml:mrow>\n                        <mml:msub>\n                          <mml:mrow>\n                            <mml:mi>L<\/mml:mi>\n                          <\/mml:mrow>\n                          <mml:mrow>\n                            <mml:mn fontstyle=\"italic\">0<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:msub>\n                      <\/mml:mrow>\n                    <\/mml:math>\n                    -norm\/\n                    <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                      <mml:mrow>\n                        <mml:msub>\n                          <mml:mrow>\n                            <mml:mi>L<\/mml:mi>\n                          <\/mml:mrow>\n                          <mml:mrow>\n                            <mml:mn fontstyle=\"italic\">1<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:msub>\n                      <\/mml:mrow>\n                    <\/mml:math>\n                    -norm constraints on sparse representation to stabilize sparse code. To decrease computation cost and to reduce false positives, regions-of-interest are determined to confine candidate infarct voxels. The proposed method has been validated on 98 consecutive patients recruited within 6 hours from onset. It is shown that the proposed method could handle well infarcts with intensity variability and ill-defined edges to yield significantly higher Dice coefficient (0.755 \u00b1 0.118) than the other two methods and their enhanced versions by confining their segmentations within the regions-of-interest (average Dice coefficient less than 0.610). The proposed method could provide a potential tool to quantify infarcts from diffusion weighted imaging at hyperacute stage with accuracy and speed to assist the decision making especially for thrombolytic therapy.\n                  <\/jats:p>","DOI":"10.1155\/2016\/2581676","type":"journal-article","created":{"date-parts":[[2016,9,22]],"date-time":"2016-09-22T17:15:28Z","timestamp":1474564528000},"page":"1-14","source":"Crossref","is-referenced-by-count":3,"title":["Segmentation of Hyperacute Cerebral Infarcts Based on Sparse Representation of Diffusion Weighted Imaging"],"prefix":"10.1155","volume":"2016","author":[{"given":"Xiaodong","family":"Zhang","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China"}]},{"given":"Shasha","family":"Jing","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan 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