问题描述
如何使用Matlab从视网膜图像中检测视杯和视盘?我想找出光圈的量度(光杯与光碟之间的距离)
How can I detect optic cup and disc from retinal image using matlab ? I want to find out the measurement of optic rim ( distance between optic cup and optic disc )
我尝试了以下代码
RGB = imread('img/A(4).jpg');
G = DialateBloodVessel(RGB);
[BW,H] = RGBThresh(G,220,60);
H = H(:,:,3);
I = edge(H,'Roberts',0.1);
imshowpair(I,G);
%%%%%%%%%% DialateBloodVessel( RGB ) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ RemovedBV ] = DialateBloodVessel( RGB )
%UNTITLED3 Summary of this function goes here
% Detailed explanation goes here
IM = RGB;
SE = strel('disk',10);
IM2 = imdilate(IM,SE);
%SE2 = strel('disk',10);
RemovedBV = imerode(IM2,SE);
end
%%%%%%%%%% RGBThresh(RGB,Ch1,Ch3) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [BW,maskedRGBImage] = RGBThresh(RGB,Ch1,Ch3)
I = RGB;
% Define thresholds for channel 1 based on histogram settings
channel1Min = Ch1;
channel1Max = 255.000;
% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.000;
channel2Max = 185.000;
% Define thresholds for channel 3 based on histogram settings
channel3Min = Ch3;
channel3Max = 255.000;
% Create mask based on chosen histogram thresholds
sliderBW = (I(:,:,1) >= channel1Min ) & (I(:,:,1) <= channel1Max) & ...
(I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
(I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);
BW = sliderBW;
% Initialize output masked image based on input image.
maskedRGBImage = RGB;
% Set background pixels where BW is false to zero.
maskedRGBImage(repmat(~BW,[1 1 3])) = 0;
end
我得到以下输出，但是在任何图像中我都需要有完美的圆圈:
I get the following output, but I need perfect circles in any image:
推荐答案
当我查看您的图片时，我注意到两件重要的事情:
When I look at your image, I notice two important things:
-
颜色并不是那么有用(通常是正确的)，因为所有东西都是红色.因此，转换为灰度是一个好主意.
Color is not that useful (which is often true), because everything is rather red. So, transforming to grayscale is a good idea.
您要选择的圆具有较大的强度变化，而不是较高的强度.因此，计算梯度可能会有用.
The circle you want to select is charaterised by a large intensity change, rather than a high intensity. Therefore, calculating gradients may be useful.
小血管的梯度也很高.因此，您的DialateBloodVessel可能会有用.
Small blood vessels have high gradients too. So, your DialateBloodVessel may be useful.
RGB = imread('0PBEL.jpg'); % load the image
% I crop the image to remove the black background (which gives high gradients too)
RGB = imcrop(RGB, floor([.2*size(RGB, 2) .2*size(RGB, 1) .6*size(RGB, 2) .6*size(RGB, 1)]));
G = rgb2gray(RGB); % convert to grayscale
G = DialateBloodVessel(G); % remove blood vessels
grad = imgradient(G); % calculate the gradient magnitude (direction is not important)
%display the (transformed) images: useful to validate method and tune parameters
figure
subplot(2, 2, 1);
imshow(RGB)
subplot(2, 2, 2);
imshow(G)
subplot(2, 2, 3);
imshow(grad, [])
subplot(2, 2, 4);
imshow(grad >= 20, [])
% calculate the centroid and radius of all the regions
stats = regionprops('table',grad >= 20,'Centroid', 'MajorAxisLength','MinorAxisLength');
centers = stats.Centroid;
diameters = mean([stats.MajorAxisLength stats.MinorAxisLength],2);
radii = diameters/2;
[maxRadii, iMax] = max(radii); % select the largest circle
subplot(2, 2, 1);
viscircles(centers(iMax, :),maxRadii); % visualise the selected circle
作为替代方案，您可以按如下方式使用内置的imfindcircles函数:
As an alternative, you can use the builtin imfindcircles functions as follows:
[centers, radii, metric] = imfindcircles(G,[50 100]);
figure
imshow(RGB)
hold on
viscircles(centers, radii,'EdgeColor','b');
请注意，此方法可能有效，但缺点是它是黑匣子.
Note that this method may work, but has the disadvantage of being a black box.
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