Suzuki Kenji
Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL 60637, USA.
Int J Biomed Imaging. 2012;2012:792079. doi: 10.1155/2012/792079. Epub 2012 Feb 28.
Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computer-aided diagnosis, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of ML is classification of objects such as lesions into certain classes (e.g., abnormal or normal, or lesions or nonlesions) based on input features (e.g., contrast and circularity) obtained from segmented object candidates. Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which use pixel/voxel values in images directly instead of features calculated from segmented objects as input information; thus, feature calculation or segmentation is not required. Because the PML can avoid errors caused by inaccurate feature calculation and segmentation which often occur for subtle or complex objects, the performance of the PML can potentially be higher for such objects than that of common classifiers (i.e., feature-based MLs). In this paper, PMLs are surveyed to make clear (a) classes of PMLs, (b) similarities and differences within (among) different PMLs and those between PMLs and feature-based MLs, (c) advantages and limitations of PMLs, and (d) their applications in medical imaging.
机器学习（ML）在医学成像领域发挥着重要作用，包括医学图像分析和计算机辅助诊断，因为诸如病变和器官等对象可能无法通过简单方程准确表示；因此，医学模式识别本质上需要“从示例中学习”。ML最常见的用途之一是基于从分割后的候选对象中获取的输入特征（例如对比度和圆形度），将病变等对象分类到特定类别（例如，异常或正常，病变或非病变）。最近，基于像素/体素的机器学习（PML）在医学图像处理/分析中出现，它直接使用图像中的像素/体素值，而不是将从分割对象计算出的特征作为输入信息；因此，不需要进行特征计算或分割。由于PML可以避免因特征计算和分割不准确而导致的错误，而这种错误在处理细微或复杂对象时经常发生，因此对于此类对象，PML的性能可能高于普通分类器（即基于特征的机器学习方法）。本文对PML进行综述，以明确（a）PML的类别，（b）不同PML之间以及PML与基于特征的ML之间的异同，（c）PML的优缺点，以及（d）它们在医学成像中的应用。
