文章基本信息

标题：Feature Extraction Values for Digital Mammograms
本地全文：下载
作者：Arpana M.A ; Prathiba Kiran
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2014
卷号：4
期号：2
页码：183-187
出版社：International Journal of Soft Computing & Engineering
摘要：Currently digital mammography is the most efficient and widely used technology for early breast cancer detection. The major diagnosing elements such as masses, lesions in the digital mammograms are noisy and of low contrast. The aim of the proposal is to enhance the mammogram images by reducing the noise using median filter, image sharpening and image smoothing. The data clustering algorithm i,e Fuzzy C means clustering is used to segment the region of interest from which various statistical, gradient and geometrical features are extracted. The features extracted from the few images of the data base are used to train the neural networks for classification. The evaluated algorithm is tested on the digital mammograms from the Mammogram Image Analysis Society (MIAS) database. The experimental results show that the breast region extracted by the presented algorithm approximately follows that extracted by an expert radiologist. The detected mass is classified as normal or abnormal. Further abnormal can be classified into benign or malignant.
关键词：Bio Medical Image processing; Mammograms;Breast Cancer; High Pass Spatial Filter; Fuzzy C means;Clustering; Median Filtering; Gradient features