标题：EVALUATE DYNAMIC KMEANS ALGORITHM FOR AUTOMATICALLY SEGMENTED DIFFERENT BREAST REGIONS IN MAMMOGRAM BASED ON DENSITY BY USING SEED REGION GROWING TECHNIQUE
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：72
期号：2
出版社：Journal of Theoretical and Applied
摘要：This paper presents a method for segment and detects the boundary of different breast tissue regions in mammograms by using dynamic Kmeans clustering algorithm after evaluate it by using Seed Based Region Growing (SBRG) techniques. Firstly, the K-means clustering is applied for dynamically and automatically divides mammogram into homogeneous regions according to the intensity of the pixel. From where automatically selected of seeds point and determined the threshold value for each region. Secondly, the region growing algorithm is applied with previously generated seeds point and threshold values as input parameter of SBRG. The main goal of this study is to evaluate the dynamic k-means clustering algorithm in the detection and segmentation of different breast tissue regions, which correspond to the density in mammograms. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and qualitative or/and quantitative evaluation of density changes. 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.
关键词：Breast Density; Breast Segmentation; Medical Image Processing; Kmeans Clustering Algorithm; Region Growing Technique
