LARODEC, Higher Institute of Management, University of Tunis, Tunisia.
Clin Imaging. 2013 May-Jun;37(3):498-503. doi: 10.1016/j.clinimag.2012.07.010. Epub 2012 Sep 5.
This article presents a novel approach for the automatic detection of lesions and selection of features on chest radiographs. We have illustrated the importance of accurate segmentation, which is based on spatial relationships between the lung structures, as a preprocessing step in a Computer Aided Diagnosis (CAD) scheme. Then, three suitable combinations of features have been identified using the forward stepwise selection method from the original images and their transformed ones. Experimental results show that our segmentation approach and the suppression of skeletal structures improve the detection accuracy. The selected features can describe efficiently different kinds of chest lesions.
本文提出了一种新颖的方法，用于自动检测胸部 X 光片上的病变和选择特征。我们已经说明了准确分割的重要性，它是计算机辅助诊断 (CAD) 方案中的预处理步骤，分割是基于肺部结构之间的空间关系。然后，使用正向逐步选择方法从原始图像及其变换图像中确定了三个合适的特征组合。实验结果表明，我们的分割方法和骨骼结构的抑制提高了检测精度。选择的特征可以有效地描述不同类型的胸部病变。
