MATLAB-mices灰度图像中的分割,对阴影不变

Question

经过2到3天的搜索,我仍然没有找到解决问题的方法.

我想创建一个没有阴影的鼠标分段.问题是如果我设法移除阴影我也删除尾部和脚部这是一个问题.阴影来自鼠标所在的竞技场墙壁.

我想从灰度图像中删除阴影,但我不知道如何做到这一点.首先,我删除了图像的背景,并获得了以下图片.

edit1:谢谢你的答案,当阴影没有碰到鼠标时效果很好.这就是我得到的结果:

从这张原始图片:

我从tif文件中提取每个帧并为每个帧应用代码.这是我使用的代码:

for k=1:1000

    %reads image
    I = imread('souris3.tif',k);

    %first stage: perform thesholding and fill holes

    seg = I >20000;
    seg = imfill(seg,'holes');

    %fixes the missing tail problem
    %extract edges, and add them to the segmentation.
    edges =  edge(I);
    seg = seg | edges;

    %fill holes (again)
    seg = imfill(seg,'holes'); 

    %find all the connected components
    CC = bwconncomp(seg,8);

    %keeps only the biggest CC
    numPixels = cellfun(@numel,CC.PixelIdxList);
    [biggest,idx] = max(numPixels);
    seg = zeros(size(edges));
    seg(CC.PixelIdxList{idx}) = 1;

    imshow(seg);

end

我选择20000作为步骤,impixelinfo因为图像在uint16,它是鼠标的平均值.

如果您想要tif文件,这是链接:

souris3.tif

谢谢你的帮忙.

Answer 1

我建议采用以下方法:

1. 对图像进行阈值处理,得到一个包含大部分鼠标身体但没有尾巴和腿的面具.
2. 使用MATLAB的imfill函数执行孔填充.在这个阶段,分割几乎是完美的,除了尾部的一部分缺失.
3. 使用边缘图来找到尾部的边界.这可以通过将边缘图添加到分割并再次执行孔填充来完成.在这个阶段只保留最大的连接组件.

码:

%reads image
I = rgb2gray(imread('mSWm4.png'));

%defines thersholds (you may want to tweak these thresholds, or find
%a way to calculate it automatically).
FIRST_STAGE_THRESHOLD = 70;
IM_BOUNDARY_RELEVANCE_THRESHOLD = 10;

%perform thesholding and fill holes, the tail is still missing
seg = I > FIRST_STAGE_THRESHOLD;
seg = imfill(seg,'holes');

%second stage fix the missing tail problem:
%extract edges from relevant areas (in which the matter is not too dark), and add them to the segmentation.
%the boundries of the image which are close enough to edges are also considered as edges
edges =  edge(I);
imageBoundries = ones(size(I));
imageBoundries(2:end-1,2:end-1) = 0;
relevantDistFromEdges = bwdist(edges) > IM_BOUNDARY_RELEVANCE_THRESHOLD;
imageBoundries(bwdist(edges) > IM_BOUNDARY_RELEVANCE_THRESHOLD) = 0;
seg = seg | (edges | imageBoundries);

%fill holes (again) and perform noise cleaning
seg = imfill(seg,'holes');
seg = getBiggestCC(imopen(seg,strel('disk',1)));

getBiggestCC函数:

function [ res ] = getBiggestCC(mask)
CC = bwconncomp(mask,8);
numPixels = cellfun(@numel,CC.PixelIdxList);
[~,idx] = max(numPixels);
res = zeros(size(mask));
res(CC.PixelIdxList{idx}) = 1;
end

结果

每个阶段的结果:

结果图像1结果:

图像2结果:

另一种观点(细分为红色):