Computerized Medical Systems, Inc.

Other

Lyn Hibbard

Derek Graham Lane

A system and method for autofusion of three-dimensional (&ldquo;3-D&rdquo;) image data volumes is described. According to the system and method, registration is effected by simultaneously displaying on a GUI at least, but preferably, two 3-D, image data volumes, and one of the 3-D image data volumes is held fixed while the other may be scaled, rotated, and translated to align homologous anatomic features. The system and method will simultaneously display the axial, sagittal, and coronal plane views of a region of interest These plane views will be of data volumes that are designed and configured for a system user to &ldquo;drag&rdquo; or &ldquo;position&rdquo; one volume to a new position in one plane view which will also simultaneously update the data volume in the other two plane views with no apparent lag time. The system and method also includes automated alignment computation based on mutual information (&ldquo;MI&rdquo;) maximization. The system and method aligns 3-D more efficiently and faster that prior image alignment methods.

Automated image fusion/alignment system and method

A system and method is disclosed for automatically computing contours representing the boundaries of objects in three-dimensional tomographic images that may be formed by computed tomography (&ldquo;CT&rdquo;), magnetic resonance imaging (&ldquo;MRI&rdquo;), positron emission tomography (&ldquo;PET&rdquo;), single proton emission computed tomography (&ldquo;SPECT&rdquo;), or other appropriate methods. The system and method begin with a sample region of the object's interior and the single region is expanded in a step-wise fashion. At each step, a contour maximally matching the region's current edge, local gray-level gradient maxima, and prior contour shapes is determined. Upon completion of region expansion, the object contour is set to that step-contour having the maximum value of an objective function summing contributions from region edges, gradient edges, and prior shapes. Both the region expansion and the boundary contour determination are formulated such that there is a guaranteed average minimum error in the determination of the contours. This contour is represented as a parametric curve in which the contour size and shape are specified by the values of the parameters. These parameters are independent variables of the objective function. The parameters also are considered to be random variables capable of encoding a distribution of contour shapes, and by assuming a particular distribution, the contribution of shape constraints to the object function can be computed. The resulting contour corresponds to the set of parameters for which the objective function is a maximum.

Autosegmentation/autocontouring system and method

An autosegmentation/autocontouring method that may be used for quickly and accurately contouring the regions and boundaries around regions for the development of cross-sections that may be linearly disposed for the three-dimensional reconstruction of an image is described and claimed. The autosegmentation/autocontouring method includes at least four steps. The first step is to digitize a CT, MRI, or other suitable image and display it on a display screen. The two-dimensional image on the display screen will include gray-scale representations of the internal organs and tissue masses of the anatomical site through which the cross-section was made. The second step is to select the interior of a ROI and draw a polygon within the boundaries of the cross-sectional view of this ROI. This polygon could also be drawn in the interior of a cancerous mass or other diseased tissue. The third step of the method of the present invention is to expand the polygon in a novel manner by iteratively testing pixels of the image on the display outside of, but adjacent to, the pixels that the polygon currently subtends. Pixels will be added to the polygon if the value of a decision rule function has a predetermined value. The expansion of the polygon is continued until none of the pixels at the perimeter of the polygon can satisfy the decision rule. Once it is found that none of the perimeter pixels satisfy the decision rule, the perimeter of the polygon is considered the boundary of the ROI. And the fourth step is that the boundary of the ROI is computed and a contour is developed based on this boundary. This same process is repeated for other ROIs that the user may select.

Growing community of inventors

St. Louis, MO, United States of America

Lyn Hibbard