Wavelets Based on Atomic Function Used in Detection and Classification of Masses in Mammography

  • Authors:

  • Cristina Juarez-Landin;Volodymyr Ponomaryov;Jose Luis Sanchez-Ramirez;Magally Martinez-Reyes;Victor Kravchenko

  • Affiliations:

  • National Polytechnic Institute, ESIME-Culhucan, Mexico, Mexico 04430 and Autonomous University of State of Mexico, Edo. de Mexico, Mexico 56615;National Polytechnic Institute, ESIME-Culhucan, Mexico, Mexico 04430;National Polytechnic Institute, ESIME-Culhucan, Mexico, Mexico 04430;Autonomous University of State of Mexico, Edo. de Mexico, Mexico 56615;Institute of Radio Engineering and Electronics of Russian Academy of Sciences, Moscow,

  • Venue:
  • MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
  • Year:
  • 2008

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Abstract

Mammography is considered the most effective method for early detection of the breast cancer. However, it is difficult for radiologists to detect microcalcification (MC) clusters and camouflages masses. The mammograms (MG) images were decomposed into several subimages using Wavelet transform (WT) based on classical and novel class Wavelets using atomic function for reducing the volume of data in the classification stage. Various regions of interest (ROIs) in the MG images were selected where input data for multilayer artificial neural network (ANN) type classifier are formed applying the WT. We used different patterns to classify the normal, MC, spiculated and circumscribed masses ROIs. The detection performance has been evaluated on MG images from the Mammographic Image Analysis Society (MIAS) database. The proposed classification scheme was shown good performance in detecting the MC clusters and masses with acceptable classification.