Automatically density based breast segmentation for mammograms by using dynamic K-means algorithm and Seed Based Region Growing
Abdelali Elmoufidi, Khalid El Fahssi, Said Jai Andaloussi, Abderrahim Sekkaki · 2015
This paper presents a method for segment and detects the boundary of different breast tissue regions in mammograms by using dynamic K-means clustering algorithm and Seed Based Region Growing (SBRG) techniques. Firstly, the K-means clustering is applied for dynamically and automatically generated the seeds points and determines the thresholds' values for each region. Secondly, the region growing algorithm is used with previously generated input parameters to divide mammogram into homogeneous regions according to the intensity of the pixel. The main goal of this method is to automatically segment and detect the boundary of different disjoint breast tissue regions in image mammography. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and qualitative and quantitative evaluation of density changes. So, using a computer-aided detection/diagnosis (CAD/CADx) system as supplement to the radiologists' assessment has an important role. In order to evaluate our proposed method we used the well-known Mammographic Image Analysis Society (MIAS) database. The obtained qualitative and quantitative results demonstrate the efficiency of this method and confirm the possibility of its use in improving the computer-aided detection/diagnosis.