Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.
Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.
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DOI：
10.1587/transinf.e96.d.772
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发表时间：
2013-04-01
影响因子：
0.7
通讯作者：
Suzuki K
中科院分区：
文献类型：
--
作者：
Suzuki K
计算机辅助检测（CADe）和诊断（CAD）已经成为医学成像领域中快速发展的活跃研究领域。机器学习（ML）在CAD中起着至关重要的作用，因为病变和器官等对象可能无法用简单的方程准确表示;因此，医学模式识别本质上需要“从示例中学习”。ML最流行的用途之一是将诸如病变候选者的对象分类为某些类别（例如，异常或正常，以及损伤或非损伤）基于输入特征（例如，对比度和面积）。ML的任务是在由输入特征形成的多维特征空间中确定用于分离类的“最佳”边界。用于分类的ML算法包括线性判别分析（LDA）、二次判别分析（QDA）、多层感知器和支持向量机（SVM）。最近，基于像素/体素的ML（PML）出现在医学图像处理/分析中，其直接使用图像中的像素/体素值，而不是从分割的病变计算的特征，作为输入信息;因此，不需要特征计算或分割。本文对用于胸部CT肺结节检测和诊断以及CT结肠成像（CTC）息肉检测的CAD方案中使用的ML技术进行了调查和评论。
Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, 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 the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine “optimal” boundaries for separating classes in the multidimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.
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影响因子：
19.7
作者：
Armato, SG;McLennan, G;Clarke, LP
通讯作者：
Clarke, LP
DOI：
10.1016/b978-012373583-6.50020-7
发表时间：
2008-01-01
期刊：
BIOMEDICAL INFORMATION TECHNOLOGY
影响因子：
--
作者：
Giger, Maryellen L.;Suzuki, Kenji
通讯作者：
Suzuki, Kenji
影响因子：
3.8
作者：
GIGER, ML;DOI, K;MACMAHON, H
通讯作者：
MACMAHON, H
影响因子：
3.8
作者：
Ge, ZY;Sahiner, B;Zhou, CA
通讯作者：
Zhou, CA
影响因子：
6.7
作者：
GIGER, ML;BAE, KT;MACMAHON, H
通讯作者：
MACMAHON, H