title: Nonrigid registration of lung CT images based on tissue features.
abstract: Nonrigid image registration is a prerequisite for various medical image process and analysis applications. Much effort has been devoted to thoracic image registration due to breathing motion. Recently, scale-invariant feature transform (SIFT) has been used in medical image registration and obtained promising results. However, SIFT is apt to detect blob features. Blobs key points are generally detected in smooth areas which may contain few diagnostic points. In general, diagnostic points used in medical image are often vessel crossing points, vascular endpoints, and tissue boundary points, which provide abundant information about vessels and can reflect the motion of lungs accurately. These points generally have high gradients as opposed to blob key points and can be detected by Harris. In this work, we proposed a hybrid feature detection method which can detect tissue features of lungs effectively based on Harris and SIFT. In addition, a novel method which can remove mismatched landmarks is also proposed. A series of thoracic CT images are tested by using the proposed algorithm, and the quantitative and qualitative evaluations show that our method is statistically significantly better than conventional SIFT method especially in the case of large deformation of lungs during respiration.
authors: Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China ; Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China.
authors: Rui
authors: R
authors: Zhang
authors: Wu
authors: W
authors: Zhou
authors: Yanjie
authors: Y
authors: Li
authors: Shaode
authors: S
authors: Yu
authors: Yaoqin
authors: Y
authors: Xie
mesh_terms: D000465:Algorithms; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D061089:Radiotherapy, Image-Guided / Q000706:statistics & numerical data; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*