• DocumentCode
    692665
  • Title

    Breast tumor detection in double views mammography based on Simple Bias

  • Author

    Shiya Zhang ; Zhongzhou Chen ; Sheng Gu ; Xihe Qiu ; Qixun Qu ; Zhiqiong Wang

  • Author_Institution
    Sino-Dutch Biomed. & Inf. Eng. Sch., Northeastern Univ., Shenyang, China
  • fYear
    2013
  • fDate
    19-20 Oct. 2013
  • Firstpage
    240
  • Lastpage
    244
  • Abstract
    Breast tumor detection is a most effective way to immunized against mammary cancer. It is known that the sort algorithm of extreme learning machine(ELM), in view of the feature model for breast X-ray image, is being applied in the computer aided detection of breast masses. On the basis of all these, it is raised in this paper that marking for the suspicious region in the double view mammography by the use of ELM, then classifying the result of double views marking by using the Simple Bias classifier and finally gaining the detection result. The experiment with 444 cases or 222 pair of X-ray mammography from Liao Ning Province Cancer Hospital shows that, the breast tumor detection in double views mammography based on Simple Bias is an available and effective way to detect breast tumor. Key Words: Extreme learning machine, Simple Bias, mammography, double views, tumor detection.
  • Keywords
    cancer; image classification; learning (artificial intelligence); mammography; medical image processing; tumours; ELM; Liao Ning Province Cancer Hospital; Simple Bias classifier; X-ray mammography; breast X-ray image; breast masses; breast tumor detection; computer aided detection; double view mammography; double view marking; extreme learning machine; feature model; mammary cancer; sort algorithm; suspicious region; Accuracy; Breast tumors; Feature extraction; Testing; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Medical Imaging Physics and Engineering (ICMIPE), 2013 IEEE International Conference on
  • Conference_Location
    Shenyang
  • Print_ISBN
    978-1-4799-6305-8
  • Type

    conf

  • DOI
    10.1109/ICMIPE.2013.6864543
  • Filename
    6864543