INVESTIGATIONS OF MULTI-VIEW CAD FOR MAMMOGRAPHY

在过去的15年中，在计算机辅助检测（CAD）乳房X线摄影异常方面取得了相当大的进展。 然而，由于当前CAD算法的性能限制，CAD是否提供净效益的问题仍未得到解决。 近年来，尽管许多团体做出了相当大的努力，但CAD性能的改善率已经下降到性能统计似乎接近渐近线的程度，这远低于乳房X线摄影师的性能。 最可能的原因是，基本上所有当前的CAD实现都是建立在传统的信号处理和模式识别方法上的，并通过检测单个图像中的特征来获得其性能。 这些功能，然后分类的一些推理机制。 可以想象（可能）的是，大多数相关的物理特征在单一的图像已被确定和利用到一定程度。该建议的假设是，当前CAD的性能限制，如CAD和乳房摄影师之间的性能差异所示，在很大程度上是由于这些算法未能利用只能通过多个图像的协同分析得出的数据。 因此，本提案的目的是扩展当前的CAD方法，以便能够从同侧视图中提取与乳房空间结构相关的信息。 我们的初步研究结果已经确定，尽管压缩引起的失真，有功能，可以自动从成对的图像，并已被证明提供的信息无法从单个图像的独立分析。这些基于多图像的特征部分地独立于乳房X射线摄影期间来自乳房压缩的组织失真。我们将调查和完善这些和其他可以识别的功能。 本研究的目的是充分利用这些类型的功能，并优化其贡献的多图像为基础的CAD算法。

Over the past 15 years, considerable progress has been made in the development of computer-aided detection (CAD) of abnormalities in mammograms. Nevertheless, because of performance limitations of current CAD algorithms, the question of whether CAD provides a net benefit remains unresolved. In recent years, despite considerable effort by many groups, the rate of improvement in CAD performance has declined to the point that performance statistics seem to be approaching an asymptote, which is well below the performance of mammographers. The most likely reason for this is that essentially all current CAD implementations are founded on traditional methods of signal processing and pattern recognition and derive their performance by detecting features in a single image. These features are then classified by some inference mechanism. It is conceivable (probable) that most of the relevant physical features in single images have been identified and exploited to some extent. The hypothesis of this proposal is that performance limitations of current CAD, as indicated by the difference in performance between CAD and mammographers, result to a large extent from the failure of these algorithms to utilize data that can only be derived by a synergistic analysis of multiple images. Thus, it is the intent of this proposal to extend current CAD methodology to enable the extraction of information related to the spatial structure of a breast from ipsilateral views. Our preliminary results have established that despite compression-induced distortion, there are features that can be derived automatically from pairs of images and have been shown to provide information not obtainable from the independent analysis of single images. These multi-image-based features are partially independent of tissue distortion from breast compression during mammography. We will investigate and refine these and other features that can be identified. The purpose of this investigation is to fully exploit these kinds of features and optimize their contributions to a multi-image-based CAD algorithm.