全乳腺超声图像中乳腺实质模式的自动分析：初步经验。
Automated analysis of breast parenchymal patterns in whole breast ultrasound images: preliminary experience.
机构信息
Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Yanagido, Gifu, Japan.
出版信息
Int J Comput Assist Radiol Surg. 2009 May;4(3):299-306. doi: 10.1007/s11548-009-0295-0. Epub 2009 Mar 14.
PURPOSE
A computerized classification scheme to recognize breast parenchymal patterns in whole breast ultrasound (US) images was developed. A preliminary evaluation of the system performance was performed.
METHODS
Breast parenchymal patterns were classified into three categories: mottled pattern (MP), intermediate pattern (IP), and atrophic pattern (AP). Each classification was defined as proposed by an experienced physician. A total of 281 image features were extracted from a volume of interest which was automatically segmented. Canonical discriminant analysis with stepwise feature selection was employed for the classification of the parenchymal patterns.
RESULTS
The classification scheme accuracy was computed to be 83.3% (10/12 cases) in MP cases, 91.7% (22/24 cases) in IP cases, 92.9% (13/14 cases) in AP cases, and 90.0% (45/50 cases) in all the cases.
CONCLUSIONS
The feasibility of an automated ultrasonography classifier for parenchymal patterns was demonstrated with promising results in whole breast US images.
目的
开发了一种用于识别全乳腺超声（US）图像中乳腺实质模式的计算机分类方案。对系统性能进行了初步评估。
方法
将乳腺实质模式分为三类：斑驳模式（MP）、中间模式（IP）和萎缩模式（AP）。每个分类均由经验丰富的医生定义。从自动分割的感兴趣区域中提取了 281 个图像特征。采用典型判别分析和逐步特征选择进行实质模式分类。
结果
MP 病例的分类方案准确率为 83.3%（10/12 例），IP 病例为 91.7%（22/24 例），AP 病例为 92.9%（13/14 例），所有病例为 90.0%（45/50 例）。
结论
在全乳腺 US 图像中，自动超声分类器用于实质模式的可行性得到了证明，结果有很大的希望。
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