Image segmentation feature selection and pattern classification for mammographic microcalcifications
Image segmentation feature selection and pattern classification for mammographic microcalcifications
复制标题
DOI：
10.1016/j.compmedimag.2005.03.002
复制
发表时间：
2005-09-01
影响因子：
5.7
通讯作者：
Wu, HK
中科院分区：
文献类型：
--
作者：
Fu, JC;Lee, SK;Wu, HK
由于X射线乳房X线照片中的微钙化是乳腺癌的主要指标，因此微钙化的检测对于开发有效的诊断系统至关重要。本文提出了一个两阶段的检测过程。在第一阶段中，使用数据驱动的封闭形式数学模型来计算疑似微钙化的位置和形状。在荷兰奈梅亨大学医院的数据库上进行测试，数据分析表明，该模型可以有效地检测微钙化的发生。所提出的数学模型不仅消除了对系统训练的需要，而且还提供了关于可疑微钙化点的边界的信息，用于进一步的特征提取。在第二阶段中，为每个疑似微钙化提取61个特征，代表纹理、空间域和谱域。从这些功能，顺序向前搜索（SFS）算法选择的分类输入向量，其中包括只对微钙化敏感的功能。两种类型的分类器-一般回归神经网络（GRNN）和支持向量机（SVM）-应用，和他们的分类性能进行比较，使用Az值的接收器操作特征曲线。对于用作输入向量的所有61个特征，测试数据集产生的Az值对于SVM为97.01%，对于GRNN为96.00%。通过SFS选择输入特征，SVM和GRNN的相应Az值分别为98.00%和97.80%。无论输入向量是否首先经过SFS特征选择，SVM的性能都优于GRNN。在这两种情况下，特征选择显著降低了输入向量的维数（SVM为82%，GRNN为59%）。此外，SFS特征选择提高了分类性能，将SVM的Az值从97.01%提高到98.00%，将GRNN的Az值从96.00%提高到97.80%。(c)2005爱思唯尔有限公司保留所有权利。
Since microcalcifications in X-ray mammograms are the primary indicator of breast cancer, detection of microcalcifications is central to the development of an effective diagnostic system. This paper proposes a two-stage detection procedure. In the first stage, a data driven, closed form mathematical model is used to calculate the location and shape of suspected microcalcifications. When tested on the Nijmegen University Hospital (Netherlands) database, data analysis shows that the proposed model can effectively detect the occurrence of microcalcifications. The proposed mathematical model not only eliminates the need for system training, but also provides information on the borders of suspected microcalcifications for further feature extraction. In the second stage, 61 features are extracted for each suspected microcalcification, representing texture, the spatial domain and the spectral domain. From these features, a sequential forward search (SFS) algorithm selects the classification input vector, which consists of features sensitive only to microcalcifications. Two types of classifiers-a general regression neural network (GRNN) and a support vector machine (SVM)-are applied, and their classification performance is compared using the Az value of the Receiver Operating Characteristic curve. For all 61 features used as input vectors, the test data set yielded Az values of 97.01% for the SVM and 96.00% for the GRNN. With input features selected by SFS, the corresponding Az values were 98.00% for the SVM and 97.80% for the GRNN. The SVM outperformed the GRNN, whether or not the input vectors first underwent SFS feature selection. In both cases, feature selection dramatically reduced the dimension of the input vectors (82% for the SVM and 59% for the GRNN). Moreover, SFS feature selection improved the classification performance, increasing the Az value from 97.01 to 98.00% for the SVM and from 96.00 to 97.80% for the GRNN. (c) 2005 Elsevier Ltd. All rights reserved.