基于模糊聚类的乳房x线图像分割

R. Boss, K. Thangavel, D. Daniel
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引用次数: 20

摘要

本文提出了一种基于模糊c均值(FCM)聚类算法的乳房x线图像分割方法。采用中值滤波器对图像进行预处理。它通常用于减少图像中的噪声。利用灰度共生矩阵(GLCM)对不同角度的乳房x线照片提取14个哈拉利克特征。通过K-Means和FCM算法对特征进行聚类,以分割感兴趣的区域进行进一步分类。根据均方误差(Mean Square error, MSE)和均方根误差(Root Mean Square error, RMSE)等误差值来衡量该算法的分割效果。在我们的实验中使用的乳房x光图像是从MIAS数据库中获得的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mammogram image segmentation using fuzzy clustering
This paper proposes mammogram image segmentation using Fuzzy C-Means (FCM) clustering algorithm. The median filter is used for pre-processing of image. It is normally used to reduce noise in an image. The 14 Haralick features are extracted from mammogram image using Gray Level Co-occurrence Matrix (GLCM) for different angles. The features are clustered by K-Means and FCM algorithms inorder to segment the region of interests for further classification. The performance of segmentation result of the proposed algorithm is measured according to the error values such as Mean Square Error (MSE) and Root Means Square Error (RMSE). The Mammogram images used in our experiment are obtained from MIAS database.
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