Automatic full-leg mosaic and display for peripheral angiography

Claim 2

An automatic full-leg mosaic for peripheral angiography comprising: image acquisition means for automatically taking a sequence of stepping images from peripheral angiography; and, processing means for automatically mosaicing said sequence of images into a full-leg display wherein said processing means comprises: global matching means for globally matching bones and vessels by measure of a ratio of overlap; and, local refinement means for compensating for any possible mismatch and nonlinear patient movement.

Claim 3

An automatic full-leg mosaic for peripheral angiography comprising image acquisition means for automatically taking a sequence of stepping images from peripheral angiography; and, processing means for mosaicing said sequence of images into a full-leg display wherein said processing means comprises: global matching means for globally matching bones and vessels by measure of a ratio of overlap; and, local refinement means for compensating for any possible mismatch and nonlinear patient movement wherein said global matching means comprises: combination means for combining multiple evidence in entire overlapped image/candidate rows; and, maximizing means for maximizing said ratio of overlap.

Claim 4

An automatic full-leg mosaic for peripheral angiography comprising image acquisition means for automatically taking a sequence of stepping images from peripheral angiography; and, processing means for mosaicing said sequence of images into a full-leg display wherein said processing means comprises: global matching means for globally matching bones and vessels by measure of a ratio of overlap; and, local refinement means for compensating for any possible mismatch and nonlinear patient movement wherein said global matching means comprises: combination means for combining multiple evidence in entire overlapped image/candidate rows; and, maximizing means for maximizing said ratio of overlap.

Claim 5

An automatic full-leg mosaic for peripheral angiography as claimed in claim 2 wherein said ratio of overlap is defined as ##EQU3## where SM 12 (x, y)=ΣΣW 1 (x, y)E 1 (x, y)W 2 (x, y)E 2 (x, y), SM 1 (x 1 , y 1 , Δx, Δy)=ΣΣW 1 (x 1 -Δx, y 1 -Δy)·E 1 (x 1 -Δx, y 1 -Δy), SM 2 (x 2 , y 2 , Δx, Δy)=W 2 (x 2 -Δx, y 2 -Δy)·E 2 (x 2 -Δx, y 2 -Δy), and SM is a similarity measure, E is an edge map, W is a weighting factor, and Δx, Δy are translational parameters.

Claim 6

An automatic full-leg mosaic for peripheral angiography as claimed in claim 1 wherein said global matching means takes evidence from multiple rows and provides a more reliable result.

Claim 7

An automatic full-leg mosaic for peripheral angiography comprising image acquisition-means for automatically taking a sequence of stepping images from peripheral angiography; and, processing means for automatically mosaicing said sequence of images into a full-leg display wherein said processing means comprises: stepping images selection means; image pair for matching selection means; edge, ridge and valley information detection means; global matching means; left and right legs separation means; intermediate matching on a single leg means; local refinement means; first decision means for deciding whether said processing is done with both legs; second decision means for deciding whether said processing is done with all image pairs; and, generating means for generating a long-leg mosaic display.

Claim 8

An automatic full-leg mosaic for peripheral angiography comprising image acquisition-means for automatically taking a sequence of stepping images from peripheral angiography; and, processing means for automatically mosaicing said sequence of images into a full-leg display wherein said processing means comprises: stepping images selection means; image pair for matching selection means; edge, ridge and valley information detection means; global matching means; left and right legs separation means; intermediate matching on a single leg means; local refinement means; first decision means for deciding whether said processing is done with both legs; second decision means for deciding whether said processing is done with all image pairs; and, generating means for generating a long-leg mosaic display.

Claim 9

An automatic full-leg mosaic for peripheral angiography as claimed in claim 5 further comprising: display and/or record means connected to said processing means.

Claim 10

An automatic full-leg mosaic for peripheral angiography as claimed in claim 6 further comprising: user interface means connected to said processing means.

Claim 11

An automatic mosaic comprising: image acquisition means for automatically taking a sequence of stepping images; processing means connected to said image acquisition means for automatically mosaicing said sequence of images into a display; and, display means connected to said processing means for displaying said mosaic wherein said processing means comprises: global matching means connected to said image acquisition means for globally matching bones and vessels by measure of a ratio of overlap; and, local refinement means connected to said global matching means for compensating for any possible mismatch and nonlinear patient movement.

Claim 12

An automatic mosaic comprising: image acquisition means for automatically taking a sequence of stepping images; processing means connected to said image acquisition means for automatically mosaicing said sequence of images into a display; and, display means connected to said processing means for displaying said mosaic wherein said processing means comprises: global matching means connected to said image acquisition means for globally matching bones and vessels by measure of a ratio of overlap; and, local refinement means connected to said global matching means for compensating for any possible mismatch and nonlinear patient movement.

Claim 13

An automatic mosaic comprising image acquisition means for automatically taking a sequence of stepping images; processing means connected to said image acquisition means for mosaicing said sequence of images into a display; and, display means connected to said processing means for displaying said mosaic wherein said processing means comprises: global matching means connected to said image acquisition means for globally matching bones and vessels by measure of a ratio of overlap; and, local refinement means connected to said global matching means for compensating for any possible mismatch and nonlinear patient movement wherein said global matching means comprises: combination means connected to said image acquisition means for combining multiple evidence in entire overlapped image/candidate rows; and, maximizing means for maximizing said ratio of overlap, wherein said ratio of overlap is defined as ##EQU4## where SM 12 (x, y)=ΣΣW 1 (x, y)E 1 (x, y)W 2 (x, y)E 2 (x, y), SM 1 (x 1 , y 1 , Δx, Δy)=ΣΣW 1 (x 1 -Δx, y 1 -Δy)·E 1 (x 1 -Δx, y 1 -Δy), SM 2 (x 2 , y 2 , Δx, Δy)=ΣΣW 2 (x 2 -Δx, y 2 -Δy)·E 2 (x 2 -Δx, y 2 -Δy), and SM is a similarity measure, E is an edge map, W is a weighting factor, and Δx, Δy are translational parameters.

Claim 14

An automatic mosaic comprising image acquisition means for automatically taking a sequence of stepping images; processing means connected to said image acquisition means for mosaicing said sequence of images into a display; and, display means connected to said processing means for displaying said mosaic wherein said processing means comprises: global matching means connected to said image acquisition means for globally matching bones and vessels by measure of a ratio of overlap; and, local refinement means connected to said global matching means for compensating for any possible mismatch and nonlinear patient movement wherein said global matching means comprises: combination means connected to said image acquisition means for combining multiple evidence in entire overlapped image/candidate rows; and, maximizing means for maximizing said ratio of overlap, wherein said ratio of overlap is defined as ##EQU4## where SM 12 (x, y)=ΣΣW 1 (x, y)E 1 (x, y)W 2 (x, y)E 2 (x, y), SM 1 (x 1 , y 1 , Δx, Δy)=ΣΣW 1 (x 1 -Δx, y 1 -Δy)·E 1 (x 1 -Δx, y 1 -Δy), SM 2 (x 2 , y 2 , Δx, Δy)=ΣΣW 2 (x 2 -Δx, y 2 -Δy)·E 2 (x 2 -Δx, y 2 -Δy), and SM is a similarity measure, E is an edge map, W is a weighting factor, and Δx, Δy are translational parameters.

Claim 15

An automatic mosaic comprising: image acquisition means; processing means for automatically mosaicing a sequence of images; and display means wherein said processing means comprises: stepping images selection means connected to said image acquisition means; image pair for matching selection means connected to said stepping images selection means; edge, ridge and valley information detection means connected to said image pair for matching selection means; global matching means connected to said edge, ridge and valley information detection means; local refinement means connected to said global matching means; decision means connected to said local refinement means; and, generating means connected to said decision means for generating a mosaic display for said display means.

Claim 16

An automatic mosaic comprising: image acquisition means; processing means for automatically mosaicing a sequence of images; and display means wherein said processing means comprises: stepping images selection means connected to said image acquisition means; image pair for matching selection means connected to said stepping images selection means; edge, ridge and valley information detection means connected to said image pair for matching selection means; global matching means connected to said edge, ridge and valley information detection means; local refinement means connected to said global matching means; decision means connected to said local refinement means; and, generating means connected to said decision means for generating a mosaic display for said display means.

Claim 17

An automatic mosaic as claimed in claim 10 further comprising: user interface means connected to said processing means.

Claim 22

A method for creating an automatic full-leg mosaic for peripheral angiography as claimed in claim 12 wherein globally matching comprises the steps of: taking evidence from multiple rows; and, providing a more reliable result.

Claim 23

A method for creating an automatic full-leg mosaic for peripheral angiography as claimed in claim 12 further comprising the steps of: displaying said full-leg mosaic; and, recording said full-leg mosaic.

Claim 24

A method for creating an automatic full-leg mosaic for peripheral angiography as claimed in claim 15 further comprising: interfacing processing means with a user.