Virtual endoscopic visualization of the colon by shape-scale signatures.
We developed a new visualization method for virtual endoscopic examination of computed tomographic (CT) colonographic data by use of shape-scale analysis. The method provides each colonic structure of interest with a unique color, thereby facilitating rapid diagnosis of the colon. Two shape features, called the local shape index and curvedness, are used for defining the shape-scale spectrum. When we map the shape index and curvedness values within CT colonographic data to the shape-scale spectrum, specific types of colonic structures are represented by unique characteristic signatures in the spectrum. The characteristic signatures of specific types of lesions can be determined by use of computer-simulated lesions or by use of clinical data sets subjected to a computerized detection scheme. The signatures are used for defining a two-dimensional color map by assignment of a unique color to each signature region. The method was evaluated visually by use of computer-simulated lesions and clinical CT colonographic data sets, as well as by an evaluation of the human observer performance in the detection of polyps without and with the use of the color maps. The results indicate that the coloring of the colon yielded by the shape-scale color maps can be used for differentiating among the chosen colonic structures. Moreover, the results indicate that the use of the shape-scale color maps can improve the performance of radiologists in the detection of polyps in CT colonography.
Näppi
Janne
J
Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA. jnappi@partners.org
Frimmel
Hans
H
Yoshida
Hiroyuki
H
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D003196:Computer Graphics; D006801:Humans; D016247:Information Storage and Retrieval; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface