Automatic segmentation and classification of fused lung Computed Tomography (CT) and Positron Emission Tomography (PET) images is presented. This system consists of four basic stages: 1). Lung image fusion process; 2). Segmentation of fused lung CT/PET images; 3). Post pre-processing; 4). Classification of fused lung images. In the first step, the lung image fusion process is made by deep learning method. At first, the input CT/PET images are decomposed by Dual Tree m-band Wavelet Transform (DTWT). The coefficients of DTWT are fused by deep learning method. This fused image of CT/PET is the input for the following steps. In the next step, the fused CT/PET images are decomposed by DTWT. It produces lower and higher frequency sub-band coefficients. Then the lower frequency components are set to zero. Then higher frequency components are used for reconstruction. Then the clustering-based thresholding method is used for segmentation. In post pre-processing step, the unwanted small regions are removed by morphological operations. Then the lung region is detected. At last, in the classification step, the features are extracted by the intensity and texture-based features. These features are classified by hybrid classifiers like Support Vector Machine (SVM) are used. The performance of the system has a higher classification accuracy of 99% using SVM classifier.

介绍了融合的肺部计算机断层扫描（CT）和正电子发射断层扫描（PET）图像的自动分割和分类。该系统包括四个基本阶段：1）。肺部图像融合过程；2）。融合肺部CT / PET图像的分割；3）。后期预处理；4）。融合肺图像的分类。第一步，通过深度学习方法进行肺图像融合过程。首先，通过双树m波段小波变换（DTWT）分解输入的CT / PET图像。DTWT的系数通过深度学习方法融合。CT / PET的融合图像是以下步骤的输入。在下一步中，通过DTWT分解融合的CT / PET图像。它产生较低和较高频率的子带系数。然后将较低的频率分量设置为零。然后，将更高频率的分量用于重建。然后将基于聚类的阈值化方法用于分割。在后处理步骤中，通过形态学操作去除不需要的小区域。然后检测肺区域。最后，在分类步骤中，通过基于强度和纹理的特征提取特征。这些功能由混合分类器分类，例如使用支持向量机（SVM）。使用SVM分类器，系统的性能具有99％的更高分类精度。通过基于强度和纹理的特征提取特征。这些功能由混合分类器分类，例如使用支持向量机（SVM）。使用SVM分类器，系统的性能具有99％的更高分类精度。通过基于强度和纹理的特征提取特征。这些功能由混合分类器分类，例如使用支持向量机（SVM）。使用SVM分类器，系统的性能具有99％的更高分类精度。