基于matlab分水岭分割进行肺癌诊断系统（GUI界面）【源码76期】

一、项目简介

本系统是一个基于分水岭算法的图像分割系统，能够对图像中的目标区域进行自动分割与标记，适用于细胞、颗粒等具有明显边界的目标提取场景。系统通过MATLAB GUI提供分步操作界面，用户可加载图像并依次查看灰度化、形态学处理、梯度计算及分水岭分割的中间结果。

核心算法流程如下：将图像灰度化并统一缩放至256像素高度，利用Sobel算子计算梯度幅值图像。通过开运算、腐蚀、形态学重建、闭运算和膨胀等一系列形态学操作得到平滑的Iobrcbr图像，在此基础上利用imregionalmax提取前景标记，经闭运算、腐蚀和面积滤波去除噪声。同时计算二值图像的距离变换并求分水岭脊线作为背景标记，将前景和背景标记强制最小化到梯度图像后执行分水岭变换，得到分割标签矩阵。最终将标签矩阵转换为彩色图像并以半透明方式叠加在原始图像上，直观展示各分割区域。

二、部分源码

function pushbutton1_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton1 (see GCBO)

% eventdata reserved – to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

global rgb

[fn,pn,~]=uigetfile('*.jpg','请选择所要识别的图像');

rgb = imread([pn fn]);

axes(handles.axes1);

imshow(rgb);title('原始图像');

% — Executes on button press in pushbutton2.

function pushbutton2_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton2 (see GCBO)

% eventdata reserved – to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

global rgb

global I

if ndims(rgb) == 3

I = rgb2gray(rgb);

else

I = rgb;

end

axes(handles.axes2);

imshow(I);title('灰度图像');

% — Executes on button press in pushbutton3.

function pushbutton3_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton3 (see GCBO)

% eventdata reserved – to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

global I

global rgb

global Iobrcbr

global gradmag

sz = size(I);

if sz(1) ~= 256

I = imresize(I, 256/sz(1));

rgb = imresize(rgb, 256/sz(1));

end

hy = fspecial('sobel');

hx = hy';

Iy = imfilter(double(I), hy, 'replicate');

Ix = imfilter(double(I), hx, 'replicate');

gradmag = sqrt(Ix.^2 + Iy.^2);

se = strel('disk', 3);

Io = imopen(I, se);

Ie = imerode(I, se);

Iobr = imreconstruct(Ie, I);

Ioc = imclose(Io, se);

Iobrd = imdilate(Iobr, se);

Iobrcbr = imreconstruct(imcomplement(Iobrd), imcomplement(Iobr));

Iobrcbr = imcomplement(Iobrcbr);

axes(handles.axes3);

imshow(Iobrcbr);title('形态学处理');

% — Executes on button press in pushbutton4.

function pushbutton4_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton4 (see GCBO)

% eventdata reserved – to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

global I

global rgb

global Lrgb

sz = size(I);

if sz(1) ~= 256

I = imresize(I, 256/sz(1));

rgb = imresize(rgb, 256/sz(1));

end

hy = fspecial('sobel');

hx = hy';

Iy = imfilter(double(I), hy, 'replicate');

Ix = imfilter(double(I), hx, 'replicate');

gradmag = sqrt(Ix.^2 + Iy.^2);

axes(handles.axes4);

imshow(gradmag, []);title('梯度图像');

% — Executes on button press in pushbutton5.

function pushbutton5_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton5 (see GCBO)

% eventdata reserved – to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

global I

global gradmag

se = strel('disk', 3);

Io = imopen(I, se);

Ie = imerode(I, se);

Iobr = imreconstruct(Ie, I);

Ioc = imclose(Io, se);

Iobrd = imdilate(Iobr, se);

Iobrcbr = imreconstruct(imcomplement(Iobrd), imcomplement(Iobr));

Iobrcbr = imcomplement(Iobrcbr);

fgm = imregionalmax(Iobrcbr);

se2 = strel(ones(3,3));

fgm2 = imclose(fgm, se2);

fgm3 = imerode(fgm2, se2);

fgm4 = bwareaopen(fgm3, 15);

bw = im2bw(Iobrcbr, graythresh(Iobrcbr));

D = bwdist(bw);

DL = watershed(D);

bgm = DL == 0;

gradmag2 = imimposemin(gradmag, bgm | fgm4);

L = watershed(gradmag2);

Lrgb = label2rgb(L, 'jet', 'w', 'shuffle');

axes(handles.axes5);

imshow(Lrgb, []);title('分割图像');

三、运行结果

四、总结

本文实现了一套基于形态学重建与分水岭算法的图像分割系统，完成灰度化、梯度计算、前景背景标记提取、强制最小化和分水岭变换的完整流程。系统通过GUI实现了各处理步骤的可视化展示，便于观察分割过程。主要不足在于：分水岭算法易产生过分割现象，对噪声和纹理复杂图像的分割效果有限；形态学参数固定，对不同类型图像的适应性不足。后续可引入标记控制优化或深度学习方法，提升复杂场景下的分割精度。

五、代码获取

接matlab程序定制和论文设计，方向如下：

图像处理｜语音识别｜图像识别｜目标检测｜深度学习｜神经网络｜强化学习｜机器学习｜通信系统｜信号处理｜时频分析｜小波降噪｜路径规划｜优化算法｜智能算法｜数据处理｜数学建模｜文献复现｜算法复现｜模型复现等

程序包运行成功，零基础的可以远程帮你运行，赠送安装包。

作为初学者，遇见不会的问题是非常正常的事情，具体代码仿真可通过主页添加 私信博主。