| 杨盈,汪天富,彭玉兰,李德玉,林江莉,罗燕.基于形态和灰度特征的乳腺肿瘤B超图像识别[J].中国医学影像技术,2005,21(11):1758~1760 |
| 基于形态和灰度特征的乳腺肿瘤B超图像识别 |
| Breast tumor image recognition of B-mode ultrasonography based on shape and gray feature analysis |
| 投稿时间：2005-08-13 修订日期：2005-09-05 |
| DOI： |
| 中文关键词: 乳腺肿瘤 傅立叶描述子 灰度特征 图像识别 |
| 英文关键词:Breast neoplasms Fourier descriptor Intensity feature Image recognition |
| 基金项目:本研究受四川省青年科技基金(05ZQ026-019)、四川省应用基础研究项目(03JY029-072-2)资助。 |
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| 中文摘要: |
| 目的 为B超诊断乳腺肿瘤建立计算机辅助诊断手段,以降低活检数以及提高诊断的准确性和客观性。方法 通过提取良性和恶性肿瘤B超图像的形态特征和灰度特征,包括傅立叶描述子,粗糙度和前后场回声比,组成特征矢量,再用k-均值聚类算法对特征矢量进行分类处理。结果 k-均值聚类算法对良性肿瘤的识别率为89.85%,对恶性肿瘤的识别正确率达78.26%。结论 本文中建立的方法能较肉眼更精确地反映良性和恶性肿瘤B超图像的特征,如果再 |
| 英文摘要: |
| Objective To provide a computer-aided method for the diagnosis of breast tumor by B-mode ultrasonic imaging. Methods The shape, margin, and intensity features including Fourier descriptor, roughness, and ratio of mean intensity were calculated from B-mode ultrasonic benign tumor and malignant tumor images. Feature vectors which indicated two classes of images were created with the three features. Then we used k-means clustering algorithm to classify vectors. Results The accuracy rates of k-means clustering algorithm were 89.85% for benign and 78.26% for malignance. Conclusion This technology could indicate the characteristics of B-mode images of benign tumor and malignant tumor more accurately than eyes did. It could greatly improve the diagnostic accuracy of breast tumor. |
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