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Feasibility Testing: Three-dimensional Tumor Mapping in Different Orientations of Automated Breast Ultrasound.
Lo, Chung-Ming; Chan, Si-Wa; Yang, Ya-Wen; Chang, Yeun-Chung; Huang, Chiun-Sheng; Jou, Yi-Sheng; Chang, Ruey-Feng.
Afiliación
  • Lo CM; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
  • Chan SW; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan.
  • Yang YW; Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Chang YC; Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Huang CS; Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. Electronic address: huangcs@ntu.edu.tw.
  • Jou YS; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
  • Chang RF; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. Electronic address: rfchang@csie.ntu.edu.tw.
Ultrasound Med Biol ; 42(5): 1201-10, 2016 May.
Article en En | MEDLINE | ID: mdl-26825468
A tumor-mapping algorithm was proposed to identify the same regions in different passes of automated breast ultrasound (ABUS). A total of 53 abnormal passes with 41 biopsy-proven tumors and 13 normal passes were collected. After computer-aided tumor detection, a mapping pair was composed of a detected region in one pass and another region in another pass. Location criteria, including the radial position as on a clock, the relative distance and the distance to the nipple, were used to extract mapping pairs with close regions. Quantitative intensity, morphology, texture and location features were then combined in a classifier for further classification. The performance of the classifier achieved a mapping rate of 80.39% (41/51), with an error rate of 5.97% (4/67). The trade-offs between the mapping and error rates were evaluated, and Az = 0.9094 was obtained. The proposed tumor-mapping algorithm was capable of automatically providing location correspondence information that would be helpful in reviews of ABUS examinations.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Reconocimiento de Normas Patrones Automatizadas / Interpretación de Imagen Asistida por Computador / Ultrasonografía Mamaria / Imagenología Tridimensional Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Ultrasound Med Biol Año: 2016 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Reino Unido
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Reconocimiento de Normas Patrones Automatizadas / Interpretación de Imagen Asistida por Computador / Ultrasonografía Mamaria / Imagenología Tridimensional Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Ultrasound Med Biol Año: 2016 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Reino Unido