Three-dimensional imaging is a well-established method in the diagnosis of diseases of the airway, the colon and the arterial System. In conjunction with fiberoptic methods and computed tomography (CT) three- dimensional imaging and reconstruction has proved to be useful in detection of tumors, obstructions, strictures and certain inflammatory lesions. Virtual endoscopy is a new method of displaying three- dimensional reconstruction of hollow anatomic structures that simulate conventional endoscopy. In contrast to conventional endoscopy, virtual endoscopy is completely noninvasive. Two approaches have been taken: the analysis of local differential geometry features, to automatically detect and assess masses of the airways, and the detection of colonic cancers and polyps. The second approach is an analysis of local surface roughness, using fractal dimension calculations. A variety of local curvature measures have been introduced and applied to both patient and artificial data reconstructions. These functions derive mainly from the estimated values of the principal curvatures (mean and Gaussian curvature) and automated detection schemes have been studied that invoke filter sets derived from the local geometry. NIH has sponsored a U.S. Govt. patent (No. 09/136,136) application for the automated detection scheme of airways and colonic cancers and polyps. Surface roughness is an important indication of anatomic structural abnormalities and is potentially detectable on virtual endoscopy. Local fractal dimension can be used to quantify surface roughness, such as arterial plaque, and has now been applied to virtual angioscopy to distinguish the thoracic aorta in a normal control from that of a patient predisposed to atherosclerosis. [1] Summers, Selbie, Malley et al. Polypoid lesions of airways: early experience with computer- assisted detection by using virtual bronchoscopy and surface curvature. Radiology, 208:331-337 (1998) [2] Summers, Johnson, Pusanik, Malley et al. Automated polyp detection for CT colonography: initial clinical assessment in 20 patients having polyps. To Appear, Radiology (1999) [3] Summers, Pusanik, Malley and Hoeg. Fractal analysis of virtual endoscopy reconstructions. Medical Imaging 1999; Physiology and Function from Multidimensional Images. Proceedings SPIE, vol. 3660: 258-269[4] U. S. Patent Application No. 09/136,136 (pending) Method for segmenting medical images and detecting surface anomalies in anatomical structures (1998); co-inventors: Summers, Selbie, Malley, Pusanik. - computer-assisted diagnosis, three dimensional imaging, cancer detection - Human Subjects
| Jerebko, Anna K; Summers, Ronald M; Malley, James D et al. (2003) Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees. Med Phys 30:52-60 |
| Jerebko, Anna K; Malley, James D; Franaszek, Marek et al. (2003) Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets. Acad Radiol 10:154-60 |
| Summers, Ronald M; Jerebko, Anna K; Franaszek, Marek et al. (2002) Colonic polyps: complementary role of computer-aided detection in CT colonography. Radiology 225:391-9 |
| Summers, R M; Beaulieu, C F; Pusanik, L M et al. (2000) Automated polyp detector for CT colonography: feasibility study. Radiology 216:284-90 |
