基于内容的图象检索在屏蔽早期胸部肿瘤Ⅹ射线测定法适用于 BI-RADS 织物分类

• 作者: Arnaldo de Albuquerque Araújo Júlia Epischina Engrácia de Oliveira Thomas M Deserno
• 来源期刊: 世界放射学杂志：英文版（电子版）
• 学科: 医学
• 关键词: COMPUTER-AIDED diagnosis CONTENT-BASED IMAGE retrieval IMAGE processing Screening MAMMOGRAPHY SINGULAR value decomposition Support vector machine
• 年，卷（期）: 2011,（1）
• 页码范围: 24-31
• 页数: 8页
• 分类号: R655.8

摘要

AIM:To present a content-based image retrieval(CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classification.METHODS:Breast density is characterized by image texture using singular value decomposition(SVD) and histograms.Pattern similarity is computed by a support vector machine(SVM) to separate the four BI-RADS tissue categories.The crucial number of remaining singular values is varied(SVD),and linear,radial,and polynomial kernels are investigated(SVM).The system is supported by a large reference database for training and evaluation.Experiments are based on 5-fold cross validation.RESULTS:Adopted from DDSM,MIAS,LLNL,and RWTH datasets,the reference database is composed of over 10000 various mammograms with unified and reliable ground truth.An average precision of 82.14% is obtained using 25 singular values(SVD),polynomial kernel and the one-against-one(SVM).CONCLUSION:Breast density characterization using SVD allied with SVM for image retrieval enable the development of a CBIR system that can effectively aid radiologists in their diagnosis.