部分图像分割的方法(matlab).doc

精品文档大津法：function y1=OTSU(image,th_set)image=imread(color1.bmp);gray=rgb2gray(image);%原图像的灰度图low_high=stretchlim(gray);%增强图像，似乎也不是一定需要gray=imadjust(gray,low_high,);% subplot(224);imshow(gray);title(after adjust);count=imhist(gray);r,t=size(gray);n=r*t;l=256;count=count/n;%各级灰度出现的概率for i=2:lif count(i)=0st=i-1;breakendend%以上循环语句实现寻找出现概率不为0的最小灰度值for i=l:-1:1if count(i)=0;nd=i-1;breakendend%实现找出出现概率不为0的最大灰度值f=count(st+1:nd+1);p=st;q=nd-st;%p和分别是灰度的起始和结束值u=0;for i=1:q;u=u+f(i)*(p+i-1);ua(i)=u;end%计算图像的平均灰度值for i=1:q;w(i)=sum(f(1:i);end%计算出选择不同k的时候，A区域的概率d=(u*w-ua).2./(w.*(1-w);%求出不同k值时类间方差y,tp=max(d);%求出最大方差对应的灰度级th=tp+p;if thth)y1(i,j)=x1(i,j);elsey1(i,j)=0;endendend%上面一段代码实现分割% figure,imshow(y1);% title(灰度门限分割的图像);程序二：clc;clear;cdD:MyDocumentsMATLABtime=now;I=imread(qr4.bmp);figure(1),imshow(I),title(p1_1.bmp);%showthepictureI2=rgb2gray(I); figure(2),imshow(I2),title(I2.bmp);%J=medfilt2(I2);figure(3),imshow(J);imwrite(J,J.bmp);MN=size(J);J1=J(1:M/2,1:fix(N/2);J2=J(1:M/2,fix(N/2)+1:N);J3=J(M/2+1:M,1:fix(N/2);J4=J(M/2+1:M,fix(N/2)+1:N);%figure(4),img=J1;T1=test_gray2bw(img);%figure(5),img=J2;T2=test_gray2bw(img);%figure(6),img=J3;T3=test_gray2bw(img);%figure(7),img=J4;T4=test_gray2bw(img);T=T1,T2;T3,T4;figure,imshow(T)%T1=edge(T,sobel);%figure,imshow(T1);%BW=edge(T,sobel);%figure,imshow(BW);functionbw_img=test_gray2bw(img)%大津法row_imgcol_img=size(img)all_pix=row_img*col_img%getprobabilityofeachpixel().count_pix=zeros(1,256)%pro_pix=fori=1:1:row_imgforj=1:1:col_imgcount_pix(1,img(i,j)+1)=count_pix(1,img(i,j)+1)+1%endendpro_pix=count_pix/all_pix%choosekvalue;max_kesi=-1T=0fork=1:1:while(i=k)wa=wa+pro_pix(1,i+1)%kiua=ua+i*pro_pix(1,i+1)i=i+1endif(wa=0.0)continue;elseua=ua/waendub=0wb=0i=k+1while(imax_kesi)max_kesi=kesiT=kendend%getbwimgbw_img=imgfori=1:1:row_imgforj=1:1:col_imgif(img(i,j)=T)bw_img(i,j)=0elsebw_img(i,j)=255endendendimwrite(bw_img,bw_img.bmp)figure(),imshow(bw_img.bmp)%,title(bw_ing)区域生长法：close all;clear all;clc;A=dicomread(im.dcm);%读入图像（医学CT图像）% seed=200,220;%选择起始位置thresh=6.3;%相似性选择阈值%A=rgb2gray(A0);%A=A0;%灰度化%A=imadjust(A,min(min(double(A)/255,max(max(double(A)/255,);figure,imshow(A,);A=double(A); %将图像灰度化y,x=getpts; %获得区域生长起始点x1=round(x); %横坐标取整y1=round(y); %纵坐标取整seed=A(x1,y1);B=A;%将A赋予Br,c=size(B); %图像尺寸 r为行数，c为列数n=r*c;%计算图像所包含点的个数pixel_seed=seed;%原图起始点灰度值q=x1 y1;%q用来装载起始位置top=1;%循环判断flagM=zeros(r,c);%建立一个与原图形同等大小的矩阵M(x1,y1)=1;%将起始点赋为1，其余为0count=1;%计数器while top=0 %也可以写成top!=0 循环结束条件r1=q(1,1);%起始点行位置c1=q(1,2);%起始点列位置p=A(r1,c1);%起始点灰度值dge=0;for i=-1:1%周围点的循环判断for j=-1:1if r1+i0&c1+j0%保证在点周围范围之内if abs(A(r1+i,c1+j)-p)=n%如果满足判定条件的点个数大于等于ntop=1;endq=q(2:top,:);top=top-1;end%subplot(1,2,1),figure,imshow(M,displayrange,);figure,%subplot(1,2,2),imshow(B,);%最后是做的一个例子迭代法：clear all;I=imread(rice.png);ZMAX=max(max(I); %取出最大灰度值ZMIN=min(min(I); %取出最小灰度值TK=(ZMAX+ZMIN)/2;bcal=1;ISIZE=size(I); %读出图像大小while(bcal) iForeground=0; %定义前景和背景数 iBackground=0; ForegroundSum=0; %定义前景和背景灰度总和 BackgroundSum=0; for i=1:ISIZE(1) %循环部分求解读下% for j=1:ISIZE(2) tmp=I(i,j); if(tmp=TK) iForeground=iForeground+1; ForegroundSum=ForegroundSum+double(tmp); %前景灰度值 else iBackground=iBackground+1; BackgroundSum=BackgroundSum+double(tmp); end end end ZO=ForegroundSum/iForeground; %计算前景和背景的平均值 ZB=BackgroundSum/iBackground; TKTmp=uint8(ZO+ZB)/2; if(TKTmp=TK ) bcal=0; else TK=TKTmp; end %当阈值不再变化的时候，说明迭代结束enddisp(strcat(迭代后的阀值：,num2str(double(TK); %显示迭代计算后阈值TKnewI=