Lung Tumor CAD Model based on Rough Set with Feature-level Fusion in PET/CT Imaging
WU Cuiying
ZHOU Tao
LU Huiling
WANG Yuanyuan
Abstract：Focusing on the issue that feature relevancy and dimension disaster problem in high-dimensional representation of PET/CT Lung tumor Region of Interesting(ROI),a lung tumor CAD model was proposed based on support vector machine(SVM) with feature-level fusion in PET/CT.Firstly,98 dimension features were extracted from lung tumor ROI, including 8 dimensional shape features, 7 dimensional gray features, 3 dimensional tamura features, 56 dimensional GLCM features and 24 dimensional frequency features.Secondly, feature subsets G1, G2, G3 were obtained by using the knowledge reduction method based on genetic algorithm in feature-level fusion and feature subsets A1, A2, A3 were obtained by using heuristic algorithm based on attribute significance in feature-level fusion, reducing the dimension of feature vectors.Thirdly, using grid search algorithm to optimize the kernel function of the SVM as the classifier, compared classification before feature-level fusion and after feature-level fusion, compared classification between based on genetic algorithm in feature-level fusion and based on attribute significance in feature-level fusion in PET/CT.Finally, 2 000 PET/CT images of lung tumors as original data,and the lung tumor CAD model based on RoughSet with feature-level fusion in PET/CT was utilized to diagnose.The experimental results show that the method can effectively improve the accuracy of diagnosis of lung tumor, and increases the feature irrelevancy to a certain extent.
Keywords：RoughsetPET/CTLung tumorFeature-level fusionComputer aided diagnosis(CAD)Support vector machine(SVM)
Publication Date：2017-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：8( 10-16,22 )