title: Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform.
abstract: BACKGROUND
  Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians.
  
  
  OBJECTIVE
  This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages. It also allows the rapid processing of the input data.
  
  
  METHODS
  In the first step, after designing a filter, the discrete wavelet transform is applied to the input images and the approximate coefficients of scaling components are constructed. Then, the different parts of image are classified in continuous spectrum. In the next step, by using adaptive K-means algorithm for initializing and smart choice of clusters' number, the appropriate threshold is selected. Finally, the suspicious cancerous mass is separated by implementing the image processing techniques.
  
  
  RESULTS
  We Received 120 mammographic images in LJPEG format, which had been scanned in Gray-Scale with 50 microns size, 3% noise and 20% INU from clinical data taken from two medical databases (mini-MIAS and DDSM). The proposed algorithm detected tumors at an acceptable level with an average accuracy of 92.32% and sensitivity of 90.24%. Also, the Kappa coefficient was approximately 0.85, which proved the suitable reliability of the system performance.
  
  
  CONCLUSIONS
  The exact positioning of the cancerous tumors allows the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth. The low PPV and high NPV of the system is a warranty of the system and both clinical specialists and patients can trust its output.
authors.affiliation: Biomedical Engineering Student, Biomedical Engineering Group, Department of Electrical and Computer Engineering, Hakim Sabzevari University of Sabzevar, Sabzevar, Iran.
authors.forename: Kh
authors.initials: Kh
authors.lastname: Rezaee
authors.affiliation: Biomedical Engineering Group, Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran.
authors.forename: J
authors.initials: J
authors.lastname: Haddadnia
keywords: Breast cancer; Discrete wavelet transform; Edge detection; K-means clustering; Winner filter