乳腺超声肿瘤图像的纹理特征分析

Qiuxia Chen, Qi Liu
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引用次数: 6

摘要

纹理是用于识别图像中感兴趣的物体或区域的重要特征之一。本文描述了基于集成空间灰度共生矩阵的一些纹理特征,并分别用模糊C-means和k - medium聚类算法说明了四种纹理特征在超声乳腺肿瘤图像分类中的有效性。实验识别准确率为72.6415%。这些结果表明,纹理特征可能对乳腺肿瘤的分类具有普遍的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Textural Feature Analysis for Ultrasound Breast Tumor Images
Texture is one of the important characteristics used in identifying objects or regions of interest in an image. This paper describes some textural features based on integrated spatial gray level co-occurrence matrix, and illustrates the effectiveness of four textural features in categorizing ultrasound breast tumor images by means of Fuzzy C-means and K-medoid clustering algorithms respectively. The experimental identification accuracy is 72.6415 percent. These results indicate that textural features probably have a general applicability for classification of breast tumors.
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