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通过定向模式分析检测既往乳房X光片中的结构扭曲。

Detection of architectural distortion in prior mammograms via analysis of oriented patterns.

作者信息

Rangayyan Rangaraj M, Banik Shantanu, Desautels J E Leo

机构信息

Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary.

出版信息

J Vis Exp. 2013 Aug 30(78):50341. doi: 10.3791/50341.

DOI:10.3791/50341
PMID:24022326
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3856936/
Abstract

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion. Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.

摘要

我们展示了基于乳腺钼靶图像中乳腺组织模式方向分析来检测间期癌病例既往乳腺钼靶片中结构扭曲的方法。我们假设在肿块或肿瘤形成之前,结构扭曲会改变乳腺钼靶图像中乳腺组织模式的正常方向。在我们方法的初始步骤中,使用Gabor滤波器和相位图分析给定乳腺钼靶片中的定向结构,以检测放射状或相交组织模式的节点样部位。然后使用节点值、分形维数以及专门设计用于表示与结构扭曲相关的毛刺状模式的角离散度测量值来表征每个检测到的部位。我们的方法使用为表征结构扭曲而开发的特征、通过二次判别分析进行模式分类以及采用留一患者法进行验证,对56例间期癌病例的106张既往乳腺钼靶片和13例正常病例的52张乳腺钼靶片的数据库进行了测试。根据自由响应接收器操作特征分析的结果,我们的方法已证明能够在乳腺癌临床诊断前(平均)15个月获取的既往乳腺钼靶片中检测到结构扭曲,每位患者约有五个假阳性时灵敏度为80%。

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本文引用的文献

1
Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms.用于检测先前乳房 X 光片中结构扭曲的定向模式离差度量。
Int J Comput Assist Radiol Surg. 2013 Jul;8(4):527-45. doi: 10.1007/s11548-012-0793-3. Epub 2012 Sep 30.
2
Four Methods to Estimate the Fractal Dimension from Self-Affine Signals.从自仿射信号估计分形维数的四种方法。
IEEE Eng Med Biol Mag. 2002 Aug 6;11(2):57-64. doi: 10.1109/51.139038.
3
Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms.用于在前次乳房 X 光片中检测结构扭曲的角扩散和熵的测量。
Int J Comput Assist Radiol Surg. 2013 Jan;8(1):121-34. doi: 10.1007/s11548-012-0681-x. Epub 2012 Mar 30.
4
A computational approach to edge detection.一种基于计算的边缘检测方法。
IEEE Trans Pattern Anal Mach Intell. 1986 Jun;8(6):679-98.
5
Optimizing Case-based detection performance in a multiview CAD system for mammography.优化多视图 CAD 系统中用于乳房 X 线摄影的基于案例的检测性能。
IEEE Trans Med Imaging. 2011 Apr;30(4):1001-9. doi: 10.1109/TMI.2011.2105886. Epub 2011 Jan 13.
6
Detection of architectural distortion in prior mammograms.先前乳房 X 光片中的结构扭曲检测。
IEEE Trans Med Imaging. 2011 Feb;30(2):279-94. doi: 10.1109/TMI.2010.2076828. Epub 2010 Sep 16.
7
Snakules: a model-based active contour algorithm for the annotation of spicules on mammography.Snakules:一种基于模型的主动轮廓算法,用于对乳腺 X 光片中的刺突进行注释。
IEEE Trans Med Imaging. 2010 Oct;29(10):1768-80. doi: 10.1109/TMI.2010.2052064. Epub 2010 Jun 7.
8
Computer-aided detection of architectural distortion in prior mammograms of interval cancer.计算机辅助检测间期癌患者既往钼靶片中的结构扭曲。
J Digit Imaging. 2010 Oct;23(5):611-31. doi: 10.1007/s10278-009-9257-x. Epub 2010 Feb 2.
9
A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows.基于线影特征的乳腺钼靶图像结构扭曲检测的初步研究。
Int J Comput Assist Radiol Surg. 2009 Jan;4(1):27-36. doi: 10.1007/s11548-008-0267-9. Epub 2008 Oct 28.
10
Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms.利用基于计算机分形的纹理分析和支持向量机对数字乳腺图像中的肿瘤病变进行特征描述和分类。
Int J Comput Assist Radiol Surg. 2009 Jan;4(1):11-25. doi: 10.1007/s11548-008-0276-8. Epub 2008 Oct 28.