Research Article Open Access

Automated Breast Cancer Diagnosis based on GVF-Snake Segmentation, Wavelet Features Extraction and Neural Network Classification

Abderrahim Sebri1, Jihène Malek2 and Rached Tourki1
  • 1 ,
  • 2 , Afganistan
Journal of Computer Science
Volume 3 No. 8, 2007, 600-607

DOI: https://doi.org/10.3844/jcssp.2007.600.607

Submitted On: 28 April 2007 Published On: 31 August 2007

How to Cite: Sebri, A., Malek, J. & Tourki, R. (2007). Automated Breast Cancer Diagnosis based on GVF-Snake Segmentation, Wavelet Features Extraction and Neural Network Classification . Journal of Computer Science, 3(8), 600-607. https://doi.org/10.3844/jcssp.2007.600.607

Abstract

Breast cancer accounts for the second most cancer diagnoses among women and the second most cancer deaths in the world. In fact, more than 11000 women die each year, all over the world, because this disease. The automatic breast cancer diagnosis is a very important purpose of medical informatics researches. Some researches has been oriented to make automatic the diagnosis at the step of mammographic diagnosis, some others treated the problem at the step of cytological diagnosis. In this work, we describes the current state of the ongoing the BC automated diagnosis research program. It is a software system that provides expert diagnosis of breast cancer based on three step of cytological image analysis. The first step is based on segmentation using an active contour for cell tracking and isolating of the nucleus in the studied image. Then from this nucleus, have been extracted some textural features using the wavelet transforms to characterize image using its texture, so that malign texture can be differentiated from benign on the assumption that tumoral texture is different from the texture of other kinds of tissues. Finally, the obtained features will be introduced as the input vector of a Multi-Layer Perceptron (MLP), to classify the images into malign and benign ones.

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Keywords

  • Breast Cancer
  • GVF-Snake
  • Segmentation
  • Wavelet feature extraction
  • Neural Network
  • MLP classification