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http://dx.doi.org/10.15701/kcgs.2018.24.5.31

Automated Classification of Ground-glass Nodules using GGN-Net based on Intensity, Texture, and Shape-Enhanced Images in Chest CT Images  

Byun, So Hyun (Department of Software Convergence, Seoul Women's University)
Jung, Julip (Department of Software Convergence, Seoul Women's University)
Hong, Helen (Department of Software Convergence, Seoul Women's University)
Song, Yong Sub (Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center)
Kim, Hyungjin (Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center)
Park, Chang Min (Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center)
Publication Information
Journal of the Korea Computer Graphics Society / v.24, no.5, 2018 , pp. 31-39 More about this Journal
Abstract
In this paper, we propose an automated method for the ground-glass nodule(GGN) classification using GGN-Net based on intensity, texture, and shape-enhanced images in chest CT images. First, we propose the utilization of image that enhances the intensity, texture, and shape information so that the input image includes the presence and size information of the solid component in GGN. Second, we propose GGN-Net which integrates and trains feature maps obtained from various input images through multiple convolution modules on the internal network. To evaluate the classification accuracy of the proposed method, we used 90 pure GGNs, 38 part-solid GGNs less than 5mm with solid component, and 23 part-solid GGNs larger than 5mm with solid component. To evaluate the effect of input image, various input image set is composed and classification results were compared. The results showed that the proposed method using the composition of intensity, texture and shape-enhanced images showed the best result with 82.75% accuracy.
Keywords
Chest CT image; Ground-glass Nodule; Nodule Classification; CNN;
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Times Cited By KSCI : 2  (Citation Analysis)
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