Tumor demarcation in mammographic images using vector quantization technique on entropy images

  • H. B. Kekre
  • Saylee M. Gharge
  • Tanuja K. Sarode
Conference paper

Abstract

Recent studies show that the interpretation of the mammograms by Radiologists gives high rates of false positive cases. Indeed the images provided by different patients have different dynamics of intensity and present a weak contrast. Moreover the size of the significant details can be very small. Several researchers have tried to develop computer aided diagnosis tools to help the radiologists in the interpretation of the mammograms for an accurate diagnosis. In order to perform a semi automated tracking of breast cancer, it is necessary to detect the presence or absence of lesions from the mammograms [1, 2].These lesions can be of various types: Nodular opacities, clear masses with lobed edges etc. They can be benign or malignant, according to their contour (sharp or blurred) – Stellar opacities (malignant tumors); micro calcifications: small calcified structures that appear as clear points on a mammogram [3, 4].

Keywords

Vector Quantization Training Vector Mammographic Image Source Symbol Color Image Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer India Pvt. Ltd 2011

Authors and Affiliations

  • H. B. Kekre
    • 1
  • Saylee M. Gharge
    • 1
  • Tanuja K. Sarode
    • 2
  1. 1.NMIMS UniversityMumbaiIndia
  2. 2.Modern College of EngineeringMumbaiIndia

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