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Computer-Aided Strain Evaluation for Acoustic Radiation Force Impulse Imaging of Breast Masses.
Lo, Chung-Ming; Chen, Yen-Po; Chang, Yeun-Chung; Lo, Chiao; Huang, Chiun-Sheng; Chang, Ruey-Feng.
Afiliação
  • Lo CM; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China.
  • Chen YP; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China.
  • Chang YC; Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China.
  • Lo C; Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China.
  • Huang CS; Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China.
  • Chang RF; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China huangcs@ntu.edu.tw rfchang@csie.ntu.edu.tw.
Ultrason Imaging ; 36(3): 151-166, 2014 Jul.
Article em En | MEDLINE | ID: mdl-24894867
ABSTRACT
Acoustic radiation force impulse (ARFI) is a newly developed elastography technique that uses acoustic radiation force to provide additional stiffness information to conventional sonography. A computer-aided diagnosis (CAD) system was proposed to automatically specify the tumor boundaries in ARFI images and quantify the statistical stiffness information to reduce user dependence. The level-set segmentation was used to delineate tumor boundaries in B-mode images, and the segmented boundaries were then mapped to the corresponding area in ARFI images for a gray-scale calculation. A total of 61 benign and 51 malignant tumors were evaluated in the experiment. The CAD system based on the proposed ARFI features achieved an accuracy of 80% (90/112), a sensitivity of 80% (41/51), and a specificity of 80% (49/61), which is significantly better than that of the quantitative B-mode features (p < 0.05). The ARFI features were further combined with the B-mode features, including shape and texture features, to further improve performance (area under the curve [AUC], 0.90 vs. 0.86). In conclusion, the CAD system based on the proposed ARFI features is a promising and efficient diagnostic method.
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Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Ultrason Imaging Ano de publicação: 2014 Tipo de documento: Article País de afiliação: China
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Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Ultrason Imaging Ano de publicação: 2014 Tipo de documento: Article País de afiliação: China