Clasificación automática de nódulos mamográficos basada en fusión de información visual multi-vista

Fabián Narváez
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Abstract

Correct mammography assessment and interpretation demands great expertise of radiologist observer and depends directly on a suitable visual analysis of mammographic findings and their correlation with radiographic features extracted from different mammographic views. In this paper, an automatic classification scheme for mammographic nodules contained on Regions of Interest (ROIs) is presented, which is based on an information fusion approach by using RoIs extracted from two different mammographic views of the same breast, a Mediolateral Oblique (MLO) view and a craniocaudal (CC) view, respectively. Once the expert radiologist selects a RoI from the two mammographic projections, those are characterized by using a multiresolution and multiscale decomposition approaches. For doing so, each RoI is projected into two different spaces defined by Zernike moments and Curvelet transform, respectively. Finally, this extracted heterogeneous information is optimally fused by using a Multiple Kernel Learning strategy based on Support vector machine scheme. The performance of the herein proposed strategy, for classifying benign and malignant nodules, was evaluated respect to the classical mammographic analysis based on only mammographic view, for which a set of 980 ROIs extracted from 490 cases and other set of 216 RoI extracted from 108 cases, which were extracted from DDSM and INBreast databases, respectively. The obtained results reported a sensitivity of 98.3% and a specificity of 94.5% respect to 96.2% and 93.1% of sensibility and specificity, respectively, and obtained by the analysis based on an only mammographic view. These results suggest that the herein proposed strategy could be useful in real clinic scenarios and could be contributing to the training of new radiologists.
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基于多视图视觉信息融合的乳房x线照相术结节自动分类
正确的乳房x光检查评估和解释需要放射科医生观察者的专业知识,并直接依赖于对乳房x光检查结果的适当视觉分析及其与从不同乳房x光检查视图中提取的放射特征的相关性。本文提出了一种基于信息融合方法的包含感兴趣区域(roi)的乳腺结节自动分类方案,该方法分别使用从同一乳房的两个不同的乳房x线照片中提取的roi,即中外侧斜位(MLO)视图和颅侧(CC)视图。一旦放射科专家从两个乳房x线照相术投影中选择RoI,就可以使用多分辨率和多尺度分解方法。为此,每个RoI被投影到两个不同的空间中,分别由Zernike矩和Curvelet变换定义。最后,利用基于支持向量机的多核学习策略对提取的异构信息进行优化融合。本文提出的良恶性结节分类策略的性能与仅基于乳房x线图像的经典乳房x线图像分析进行比较,其中从490例中提取了980个RoI,从DDSM和INBreast数据库中分别提取了108例的216个RoI。所获得的结果报告敏感性为98.3%,特异性为94.5%,敏感性和特异性分别为96.2%和93.1%,并且仅基于乳房x线片视图进行分析。这些结果表明,本文提出的策略可能在实际的临床场景中有用,并可能有助于培训新的放射科医生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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