Detection of Ground Glass Opacities in Lung CT Images Using Gabor Filters and Neural Networks

This paper aims at developing a CAD system used for the detection of Ground Glass Opacity (GGO) nodules in chest CT images. In our scheme, we apply Gabor filter on the CT image in order to enhance the detection process. After this we perform some morphological operations including threshold process and labeling to extract the objects having high intensity values. Then, some feature analysis is used to examine these objects to decide which of them are likely to be cancer candidates. Following the feature analysis, a template matching between the potential cancer candidates and some Gaussian reference models is performed to determine the similarity between them. The algorithm was applied on 715 slices containing 25 GGO nodules and achieved detection sensitivity of 92% with False Positive (FP) rate of 0.76 FP/slice. Finally, we used an Artificial Neural Network (ANN) to reduce the number of FP findings. After using ANN, we were able to reduce the FP rate to 0.25 FP/slice but at the expense of decreasing the detection sensitivity to 84%