1. Supervoxel Graph Cuts: An Effective Method for GGO Candidate Regions Extraction on CT Images
- Author
-
Hyoungseop Kim, Takatoshi Aoki, Shoji Kido, Yujie Li, Huimin Lu, Seiichi Murakami, Masashi Kondo, and Joo Kooi Tan
- Subjects
Artifact (error) ,Matching (graph theory) ,Computer science ,business.industry ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,Filter (signal processing) ,computer.software_genre ,Computer Science Applications ,Human-Computer Interaction ,Support vector machine ,Hardware and Architecture ,Voxel ,Region of interest ,Cut ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Electrical and Electronic Engineering ,Elastic matching ,business ,computer - Abstract
In this article, a method to reduce artifacts on temporal difference images is introduced. The proposed method uses a nonrigid registration method for ground glass opacification (GGO), which is light in concentration and difficult to detect early. In this method, global matching, local matching, and three-dimensional (3D) elastic matching are performed on the current and previous images, and an initial temporal subtraction image is generated. After that, we use an Iris filter, which is the gradient vector concentration degree filter, to determine the initial GGO candidate regions and use supervoxel and graph cuts to segment region of interest in the 3D images. For each extracted region, a support vector machine is used to reduce the oversegmentation. The voxel matching is applied to generate the final temporal difference image, emphasizing the GGO regions while reducing the artifact.
- Published
- 2020
- Full Text
- View/download PDF