问题
I have to do a project using 2D CT images and segment liver and tumor in it using Matlab(only). Initially i have to segment liver region alone. I use region growing for liver segmentation. It gets seed point as input.
The output is an image with a boundary for liver region. Now I need the region that is surrounded by the boundary alone.
My program has a main program and a regionGrowing.m function. As I'm a new user am not allowed to post images. If you do need images I will mail you. Kindly help me.
% mainreg.m
IR=imread('nfliver5.jpg');
figure, imshow(IR), hold all
poly = regionGrowing(IR,[],15,1200); % click somewhere inside the liver
plot(poly(:,1), poly(:,2), 'LineWidth', 2, 'Color', [1 1 1])
%regionGrowing.m
function [P, J] = regionGrowing(cIM, initPos, thresVal, maxDist, tfMean, tfFillHoles, tfSimplify)
% REGIONGROWING Region growing algorithm for 2D/3D grayscale images
%
% Syntax:
% P = regionGrowing();
% P = regionGrowing(cIM);
% P = regionGrowing(cIM, initPos)
% P = regionGrowing(..., thresVal, maxDist, tfMean, tfFillHoles, tfSimpl)
% [P, J] = regionGrowing(...);
%
% Inputs:
% cIM: 2D/3D grayscale matrix {current image}
% initPos: Coordinates for initial seed position {ginput position}
% thresVal: Absolute threshold level to be included {5% of max-min}
% maxDist: Maximum distance to the initial position in [px] {Inf}
% tfMean: Updates the initial value to the region mean (slow) {false}
% tfFillHoles: Fills enclosed holes in the binary mask {true}
% tfSimplify: Reduces the number of vertices {true, if dpsimplify exists}
%
% Outputs:
% P: VxN array (with V number of vertices, N number of dimensions)
% P is the enclosing polygon for all associated pixel/voxel
% J: Binary mask (with the same size as the input image) indicating
% 1 (true) for associated pixel/voxel and 0 (false) for outside
%
% Examples:
% % 2D Example
% load example
% figure, imshow(cIM, [0 1500]), hold all
% poly = regionGrowing(cIM, [], 300); % click somewhere inside the lungs
% plot(poly(:,1), poly(:,2), 'LineWidth', 2)
%
% % 3D Example
% load mri
% poly = regionGrowing(squeeze(D), [66,55,13], 60, Inf, [], true, false);
% plot3(poly(:,1), poly(:,2), poly(:,3), 'x', 'LineWidth', 2)
%
% Requirements:
% TheMathWorks Image Processing Toolbox for bwboundaries() and axes2pix()
% Optional: Line Simplification by Wolfgang Schwanghart to reduce the
% number of polygon vertices (see the MATLAB FileExchange)
%
% Remarks:
% The queue is not preallocated and the region mean computation is slow.
% I haven't implemented a preallocation nor a queue counter yet for the
% sake of clarity, however this would be of course more efficient.
%
% Author:
% Daniel Kellner, 2011, braggpeaks{}googlemail.com
% History: v1.00: 2011/08/14
% error checking on input arguments
if nargin > 7
error('Wrong number of input arguments!')
end
if ~exist('cIM', 'var')
himage = findobj('Type', 'image');
if isempty(himage) || length(himage) > 1
error('Please define one of the current images!')
end
cIM = get(himage, 'CData');
end
if ~exist('initPos', 'var') || isempty(initPos)
himage = findobj('Type', 'image');
if isempty(himage)
himage = imshow(cIM, []);
end
% graphical user input for the initial position
p = ginput(1);
% get the pixel position concerning to the current axes coordinates
initPos(1) = round(axes2pix(size(cIM, 2), get(himage, 'XData'), p(2)));
initPos(2) = round(axes2pix(size(cIM, 1), get(himage, 'YData'), p(1)));
end
if ~exist('thresVal', 'var') || isempty(thresVal)
thresVal = double((max(cIM(:)) - min(cIM(:)))) * 0.05;
end
if ~exist('maxDist', 'var') || isempty(maxDist)
maxDist = Inf;
end
if ~exist('tfMean', 'var') || isempty(tfMean)
tfMean = false;
end
if ~exist('tfFillHoles', 'var')
tfFillHoles = true;
end
if isequal(ndims(cIM), 2)
initPos(3) = 1;
elseif isequal(ndims(cIM),1) || ndims(cIM) > 3
error('There are only 2D images and 3D image sets allowed!')
end
[nRow, nCol, nSli] = size(cIM);
if initPos(1) < 1 || initPos(2) < 1 ||...
initPos(1) > nRow || initPos(2) > nCol
error('Initial position out of bounds, please try again!')
end
if thresVal < 0 || maxDist < 0
error('Threshold and maximum distance values must be positive!')
end
if ~isempty(which('dpsimplify.m'))
if ~exist('tfSimplify', 'var')
tfSimplify = true;
end
simplifyTolerance = 1;
else
tfSimplify = false;
end
% initial pixel value
regVal = double(cIM(initPos(1), initPos(2), initPos(3)));
% text output with initial parameters
disp(['RegionGrowing Opening: Initial position (' num2str(initPos(1))...
'|' num2str(initPos(2)) '|' num2str(initPos(3)) ') with '...
num2str(regVal) ' as initial pixel value!'])
% preallocate array
J = false(nRow, nCol, nSli);
% add the initial pixel to the queue
queue = [initPos(1), initPos(2), initPos(3)];
%%% START OF REGION GROWING ALGORITHM
while size(queue, 1)
% the first queue position determines the new values
xv = queue(1,1);
yv = queue(1,2);
zv = queue(1,3);
% .. and delete the first queue position
queue(1,:) = [];
% check the neighbors for the current position
for i = -1:1
for j = -1:1
for k = -1:1
if xv+i > 0 && xv+i <= nRow &&... % within the x-bounds?
yv+j > 0 && yv+j <= nCol &&... % within the y-bounds?
zv+k > 0 && zv+k <= nSli &&... % within the z-bounds?
any([i, j, k]) &&... % i/j/k of (0/0/0) is redundant!
~J(xv+i, yv+j, zv+k) &&... % pixelposition already set?
sqrt( (xv+i-initPos(1))^2 +...
(yv+j-initPos(2))^2 +...
(zv+k-initPos(3))^2 ) < maxDist &&... % within distance?
cIM(xv+i, yv+j, zv+k) <= (regVal + thresVal) &&...% within range
cIM(xv+i, yv+j, zv+k) >= (regVal - thresVal) % of the threshold?
% current pixel is true, if all properties are fullfilled
J(xv+i, yv+j, zv+k) = true;
% add the current pixel to the computation queue (recursive)
queue(end+1,:) = [xv+i, yv+j, zv+k];
if tfMean
regVal = mean(mean(cIM(J > 0))); % --> slow!
end
end
end
end
end
end
%%% END OF REGION GROWING ALGORITHM
% loop through each slice, fill holes and extract the polygon vertices
P = [];
for cSli = 1:nSli
if ~any(J(:,:,cSli))
continue
end
% use bwboundaries() to extract the enclosing polygon
if tfFillHoles
% fill the holes inside the mask
J(:,:,cSli) = imfill(J(:,:,cSli), 'holes');
B = bwboundaries(J(:,:,cSli), 8, 'noholes');
else
B = bwboundaries(J(:,:,cSli));
end
newVertices = [B{1}(:,2), B{1}(:,1)];
% simplify the polygon via Line Simplification
if tfSimplify
newVertices = dpsimplify(newVertices, simplifyTolerance);
end
% number of new vertices to be added
nNew = size(newVertices, 1);
% append the new vertices to the existing polygon matrix
if isequal(nSli, 1) % 2D
P(end+1:end+nNew, :) = newVertices;
else % 3D
P(end+1:end+nNew, :) = [newVertices, repmat(cSli, nNew, 1)];
end
end
% text output with final number of vertices
disp(['RegionGrowing Ending: Found ' num2str(length(find(J)))...
' pixels within the threshold range (' num2str(size(P, 1))...
' polygon vertices)!'])
回答1:
If I understand you correctly, you have a binary image of the boundary of the kidney and now need to set the inside of the boundary to 1s. To do this, you can use the imfill() function with the 'holes' setting on.
BW2 = imfill(BW,'holes');
EDIT: Looking at the code, it seems that it does what you want already.
% Outputs:
% J: Binary mask (with the same size as the input image) indicating
% 1 (true) for associated pixel/voxel and 0 (false) for outside
so you just need to get the second output as well:
IR=imread('nfliver5.jpg');
figure, imshow(IR), hold all
[poly im] = regionGrowing(IR,[],15,1200); % click somewhere inside the liver
imshow(im,[])
Now im is a binary image with your segmented region.
EDIT2:
Once you have the binary image im, you can simply use element-wise multiplication to remove all parts of the orignal image outside the segmented region.
SEG = IR.*im;
imshow(SEG,[])
EDIT3:
For 3D images, you need to specify the coordinates manually, and not by using the mouse. This is because the mouse only gives us 2 coordinates (x and y) and you need 3 (x,y and z). So just find the coordinates you need by looking at the image, and then choosing an appropriate z coordinate.
%Example coordinates,
coordinates = [100 100 5]
poly = regionGrowing(squeeze(IR), coordinates, 60, Inf, [], true, false);
来源：https://stackoverflow.com/questions/9442932/extracting-image-region-within-boundary