A detection and classification method of a shape in a medical image is provided. It is based on generating a plurality of 2-D sections through a 3-D volume in the medical image. In general, there are two steps. The first step is feature estimation to generate shape signatures for candidate volumes containing candidate shapes. The feature estimation method computes descriptors of objects or of their images. The second general step involves classification of these shape signatures for diagnosis. A classifier contains, builds and/or trains a database of descriptors for previously seen shapes, and then maps descriptors of novel images to categories corresponding to previously seen shapes or classes of shapes.
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is cross-referenced to and claims priority from U.S. Provisional Application 60/415,269 filed Sep. 30, 2002, which is hereby incorporated by reference.
The invention relates to a method for processing of a three-dimensional image data set, wherein the three-dimensional image data set is converted to a data set suitable for a two-dimensional image reproduction. The invention further relates to apparatuses for performing the required calculations and/or for reproduction of the data representations. The invention is particularly appropriate for medical applications of endoscopy, in particular, coloscopy.
Certain embodiments of the present invention provide a system and method for identifying stool particles in virtual dissection data for a colon. A shape classification may be determined for a segmented colon by three-dimensional filtering of a prone data set and a supine data set. The shape classification may be mapped onto a prone virtual dissection image and a supine virtual dissection image. The prone data set and the supine data set may be registered using one-dimensional registration to determine a registration. Shapes may be localized based on the shape classification and the registration for the prone virtual dissection and the supine virtual dissection. A distance metric may be applied to the localized shapes to identify stool particles. The identified stool particles may be suppressed. A prone virtual dissected image and a supine virtual dissected image may be displayed having the stool particles suppressed.
