python - How to transform Ultrasound images for simulation of CT images?

Question

so I'd like to simulate CT images from ultrasound images using GAN and I am currently working on the data preparation.

By nature of the ultrasound these images are stored in a cone shaped kind of form:

But what I want to have is the image in the following form:

I belief it is easier to simulate the CT image that way.

I am using simple ITK. I guess this should be a common transformation. Is there maybe a filter from sITK that I am not aware of? Or is there an other simple way to do this transformation?

question from:https://stackoverflow.com/questions/65941062/how-to-transform-ultrasound-images-for-simulation-of-ct-images

Answer 1

The homography idea didn't work so this won't serve as an answer, but hopefully some of this is still helpful.

I basically targeted six keypoints and tried to rectify them. However, the homography didn't handle the cylindrical curve at the top and bottom.

import cv2
import numpy as np

# load image
img = cv2.imread("original.png");

# chop bottom (there's a weird gray band down there)
h, w = img.shape[:2];
img = img[:h-10, :, :];

# convert color
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY);
thresh = cv2.inRange(gray, 30, 255);

# split
h, w = gray.shape;
half = int(w/2);
left = gray[:,:half];
right = gray[:,half:];

# find corners
threshold = 30;
# top left
stop = False;
tl = [-1, -1];
for y in range(h):
    for x in range(half):
        if left[y,x] > threshold:
            tl = [x, y];
            stop = True;
            break;
    if stop:
        break;

# top right
stop = False;
tr = [-1, -1];
for y in range(h):
    for x in range(half):
        if right[y, x] > threshold:
            tr = [x + half, y];
            stop = True;
            break;
    if stop:
        break;

# bottom left
bl = [-1, -1];
stop = False;
for x in range(half):
    for y in range(h):
        if left[y, x] > threshold:
            bl = [x, y];
            stop = True;
            break;
    if stop:
        break;

# bottom right
br = [-1, -1];
stop = False;
for x in range(half - 1, 0, -1):
    for y in range(h):
        if right[y, x] > threshold:
            br = [x + half, y];
            stop = True;
            break;
    if stop:
        break;

# middle top
mt = [-1, -1];
for y in range(h):
    if right[y, 0] > threshold:
        mt = [half, y];

# middle bottom
mb = [-1, -1];
for y in range(h-1, 0, -1):
    if right[y, 0] > threshold:
        mb = [half, y];


# corners
corners = [];
corners.append(tl);
corners.append(tr);
corners.append(br);
corners.append(bl);
corners.append(mt);
corners.append(mb);

# draw points
for p in corners:
    print(p);
    tup = (p[0], p[1]);
    img = cv2.circle(img, tup, 10, (0,0,255), -1);
# img = cv2.circle(img, (100, 100), 1000, (0, 0, 255), -1);
print("Res: " + str([w,h]));

# create homography destination
targets = [];
targets.append([0, 0]); # tl
targets.append([w, 0]); # tr
targets.append([w, h]); # br
targets.append([0, h]); # bl
targets.append([half, 0]); # mt
targets.append([half, h]); # mb

# make blank
corners = np.array(corners);
targets = np.array(targets);
hmat, ret = cv2.findHomography(corners, targets);

# warp image
warped = cv2.warpPerspective(img, hmat, (w, h));

# show
cv2.imshow("img", img);
cv2.imshow("thresh", thresh);
cv2.imshow("warped", warped);
cv2.waitKey(0);