Automatic Detection and Characterization of Funnel Chest Based on Spiral CT
A method was proposed in order to process and classify CT slices representing funnel chest deformities. A manually chosen CT slice was processed to detect the inner curvature of the chest for characterization. Normalized data from the detected inner curvature was gained and saved next to a manually-given deformity type for further classifications. Based on the multiple correlations of the values gained from the inner curvature, a hierarchical classification was performed on 199 patient data. Results have shown that the calculated values gained from the inner curvature can accurately characterize the deformity type of the chest. Since minimal user interaction was necessary to detect and characterize the inner curvature, our method is considered to be an effective automated procedure for funnel chest deformity classifications.
Laszlo Papp Reka Juhasz Sonja Travar Alexander Kolli Erich Sorantin
Department of Radiology and Nuclear Medicine,UK-SH Campus Kiel,Germany Mediso Medical Imaging System Department of Image Processing and Computer Graphics,University of Szeged,Hungary Clinical Center Vojvodina,University of Novi Sad,Serbia Research Unit for Digital Information and Image Processing,University Clinic for Radiology,Medical U
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）