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Method for increasing the robustness of computer-aided diagnosis to image processing uncertainties

机译:提高计算机辅助诊断对图像处理不确定性的鲁棒性的方法

摘要

A classifier (20) is trained by a feature matrix (18, 18′) made up of feature vectors (F11, . . . , Fkm). The feature vectors are generated by operating on each of a plurality (k) of training image data sets with each of a plurality (m) of image processing algorithms (121, . . . , 12m) to generate processed and segmented images. Features of the segmented regions are extracted (14) to generate the feature vectors. In this manner, the classifier is trained with data generated with a variety of image processing algorithms.
机译:分类器( 20 )通过由特征向量(F 1 1)组成的特征矩阵( 18,18 ')进行训练,...,Fk m )。特征向量是通过使用多个(m)图像处理算法( 12 1 < B>,...,12 m )来生成经过处理和分割的图像。提取分割区域的特征( 14 )以生成特征向量。以这种方式,利用由各种图像处理算法生成的数据来训练分类器。

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