• DocumentCode
    631540
  • Title

    Data mining supported analysis of medical atomic force microscopy images

  • Author

    Hutterer, Stephan ; Mayr, Simon ; Zauner, Gunther ; Silye, Rene ; Schilcher, Kurt

  • Author_Institution
    Sch. of Eng. & Environ. Sci., Univ. of Appl. Sci. Upper Austria, Wels, Austria
  • fYear
    2013
  • fDate
    16-19 April 2013
  • Firstpage
    99
  • Lastpage
    104
  • Abstract
    Taking a look at actual developments in the field of bioinformatics, computer support in different fields of medicine and biology is an increasing field of interest. Especially for recognition and classification of various kinds of diseases, researchers already identified the usage of data mining techniques as supporting tool for computer supported analysis and diagnosis. In order to build such tools, highly accurate and machine-readable data is needed. Therefore, atomic force microscopy (AFM) is shown within this paper as measurement technology, that provides true 3-D data of any tissue and thus is able to build the fundament for further computational processing steps. Within two practical examples dealing with brain tumor tissue on the one hand and myocardial muscle tissue on the other hand, data mining supported analysis of AFM images will be demonstrated. The combination of data mining techniques with AFM measurements at nanoscale therefore forms a promising fundament for future computer enabled support systems in medicine and biology.
  • Keywords
    atomic force microscopy; bioinformatics; biomedical optical imaging; brain; computer aided analysis; data mining; diseases; image classification; medical image processing; muscle; pattern recognition; tumours; AFM image; bioinformatics; biology; brain tumor tissue; computational processing step; computer enabled support system; computer supported analysis; computer supported diagnosis; data mining supported analysis; data mining technique; disease classification; disease recognition; machine-readable data; measurement technology; medical atomic force microscopy image; medicine; myocardial muscle tissue; true tissue 3-D data; Accuracy; Algorithm design and analysis; Feature extraction; Microscopy; Muscles; Tumors; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence in Healthcare and e-health (CICARE), 2013 IEEE Symposium on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4673-5882-8
  • Type

    conf

  • DOI
    10.1109/CICARE.2013.6583076
  • Filename
    6583076