利用磁共振成像的统计分析和形状测量进行乳腺病变鉴别

Breast lesion discrimination using statistical analysis and shape measures on magnetic resonance imagery.

作者信息

Adams A H, Brookeman J R, Merickel M B

机构信息

University of Virginia, Department of Biomedical Engineering, Charlottesville 22908.

出版信息

Comput Med Imaging Graph. 1991 Sep-Oct;15(5):339-49. doi: 10.1016/0895-6111(91)90142-i.

DOI:10.1016/0895-6111(91)90142-i

PMID:1661638

Abstract

Magnetic resonance images of intact human breast tissue are evaluated using statistical measures and shape analysis. In this paper, the Mahalanobis distance measurement and a related F-statistical value demonstrate that breast lesions are statistically separable from normal breast tissue. The minimum set of parameters to provide first order statistical separability between fibroadenomas, cysts, and carcinomas are T1-weighted, T2-weighted, and Dixon opposed pulse sequences. Tumor shape is quantified by development of a compactness measure and a spatial frequency analysis of the lesion boundary. Malignant lesions are shown to be separable from benign lesions based on quantitative shape measures.

摘要

利用统计测量和形状分析对完整的人类乳腺组织的磁共振图像进行评估。在本文中，马氏距离测量和相关的F统计值表明，乳腺病变在统计学上可与正常乳腺组织区分开来。在纤维腺瘤、囊肿和癌之间提供一阶统计可分离性的最小参数集是T1加权、T2加权和狄克逊反向脉冲序列。通过开发紧凑度测量和病变边界的空间频率分析来量化肿瘤形状。基于定量形状测量，恶性病变显示可与良性病变区分开来。