title: Feature-guided shape-based image interpolation.
abstract: A feature-guided image interpolation scheme is presented. It is an effective and improved, shape-based interpolation method used for interpolating image slices in medical applications. The proposed method integrates feature line-segments to guide the shape-based method for better shape interpolation. An automatic method for finding these line segments is given. The proposed feature-guided shape-based method can manage translation, rotation and scaling situations when the slices have similar shapes. It can also interpolate intermediate shapes when the successive slices do not have similar shapes. This method is experimentally evaluated using artificial and real two-dimensional and three-dimensional data. The proposed method generated satisfactory interpolated results in these experiments. We demonstrate the practicality, effectiveness and reproducibility of the proposed method for interpolating medical images.
authors.affiliation: Computer Graphics Group/Visual System Laboratory, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC. tonylee@mail.ncku.edu.tw
authors.forename: Tong-Yee
authors.initials: TY
authors.lastname: Lee
authors.forename: Chao-Hung
authors.initials: CH
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D008963:Molar / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D011786:Quality Control; D011859:Radiography; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity