• DocumentCode
    1849569
  • Title

    Cancer detection from pathological images using Higher-order Local Autocorrelation feature

  • Author

    Jia Qu ; Nosato, Hirokazu ; Sakanashi, Hidenori ; Terai, K. ; Hiruta, N.

  • Author_Institution
    Dept. of Intell. Interaction Technol., Univ. of Tsukuba, Tsukuba, Japan
  • Volume
    2
  • fYear
    2012
  • fDate
    21-25 Oct. 2012
  • Firstpage
    1198
  • Lastpage
    1201
  • Abstract
    Pathological diagnosis, plays a very crucial role in cancer examination. However, because of the shortage of pathologists, increasing working burden has been imposed on pathologists. Aiming to reduce pathologist´s burden and improve the reliability of cancer diagnosis, an innovative cancer detection method for pathological images and its extended approach for advanced ability is presented in this paper. The experiments demonstrate the effectiveness of the proposed method in cancer detection and cancer location estimation.
  • Keywords
    cancer; correlation methods; medical image processing; patient diagnosis; reliability; cancer detection; cancer diagnosis; cancer location estimation; higher-order local autocorrelation feature; pathological diagnosis; pathological images; reliability; HLAC; anomaly detection; pathological images;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing (ICSP), 2012 IEEE 11th International Conference on
  • Conference_Location
    Beijing
  • ISSN
    2164-5221
  • Print_ISBN
    978-1-4673-2196-9
  • Type

    conf

  • DOI
    10.1109/ICoSP.2012.6491791
  • Filename
    6491791