Automatic Detection of Fiducial Marker Center Based on Shape Index and Curvedness
MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Adaptive volumetric detection of lesions for minimal-preparation dual-energy CT colonography
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
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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.