A COMPUTER-AIDED SYSTEM FOR MASS DETECTION AND CLASSIFICATION IN DIGITIZED MAMMOGRAMS

Authors:
  • SYSheng-Chih Yang
    Sheng-Chih Yang
  • CWChuin-Mu Wang
    Chuin-Mu Wang
  • SSSHENG-CHIH YANGDepartment of Electrical Engineering, National Cheng Kung University, Tainan, TaiwanDepartment of Computer Science and Information Engineering, National Chin Yi Institute of Technology, Taichung, TaiwanCHUIN-MU WANGDepartment of Computer Sc
    SHENG-CHIH YANGDepartment of Electrical Engineering, National Cheng Kung University, Tainan, TaiwanDepartment of Computer Science and Information Engineering, National Chin Yi Institute of Technology, Taichung, TaiwanCHUIN-MU WANGDepartment of Computer Sc
  • YHYue-Jing He
    Yue-Jing He
  • YWYi-Jyun Wun
    Yi-Jyun Wun
  • NANorhene Gargouri Ben Ayed
    Norhene Gargouri Ben Ayed
  • AMAlima Dammak Masmoudi
    Alima Dammak Masmoudi
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Abstract
This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.

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