Matlab醫(yī)學(xué)圖像分割區(qū)域生長(zhǎng)

-.z.實(shí)驗(yàn)十醫(yī)學(xué)圖像分割（二）實(shí)驗(yàn)?zāi)康模毫私鈭D像分割的基本理論和方法；掌握閾值分割的方法和閾值的選擇；掌握基于分水嶺分割的原理和應(yīng)用；實(shí)驗(yàn)容：區(qū)域生長(zhǎng)法利用圖像像素間的相似性進(jìn)行分割，調(diào)用regiongrow函數(shù)對(duì)圖像weld.tif進(jìn)行處理，注意參數(shù)中S（種子值），T（閾值）的選擇對(duì)分割效果的影響。S=255，T＝65和S＝255T＝150和S＝150，T＝65三組值進(jìn)行處理，理解在區(qū)域生長(zhǎng)法的原理。同時(shí)對(duì)liver.bmp，自己選擇合適的S和T，以較好得分割出肝臟。代碼1：closeall;f=imread('weld.tif');figure(1),imshow(f),title('原圖');[g1,NR]=regiongrow(f,255,65);[g2,NR]=regiongrow(f,255,150);[g3,NR]=regiongrow(f,150,65);figure(2),imshow(g1),title('S=255,T=65');figure(3),imshow(g2),title('S=255,T=150');figure(4),imshow(g3),title('S=150,T=65');[y,*]=ginput();*=floor(*);y=floor(y);G=zeros(size(g));G(g3==g3(*,y))=255;結(jié)果：代碼2：closeall;f=imread('liver1.bmp');figure(1),imshow(f),title('原圖');g=regiongrow(f,255,135);%肝臟分離figure(2),imshow(g),title('S=255,T=135');[y,*]=ginput();*=floor(*);y=floor(y);G=zeros(size(g));G(g==g(*,y))=255;figure(3),imshow(G);結(jié)果：編寫m文件，實(shí)現(xiàn)單一型或質(zhì)心型區(qū)域生長(zhǎng)算法。由用戶通過(guò)ginput函數(shù)選擇種子點(diǎn)，然后根據(jù)用戶輸入的閾值T進(jìn)行區(qū)域生長(zhǎng)。輸入為：待分割的圖片f和閾值T，輸出為分割后的二值圖片g。代碼1：（單一型）function[a]=regiongrow2(f,T)imshow(f);se=[0,1,0;1,1,1;0,1,0];[y,*]=ginput();*=floor(*);y=floor(y);a=im2bw(zeros(size(f)));a(*,y)=1;while1b=imdilate(a,se);P=b-a;find(P);inde*=find(P);n=size(inde*);fori=1:nt=abs(f(*,y)-f(inde*(i)));ift>=Tb(inde*(i))=0;P(inde*(i))=0;endenda=b;ifsum(sum(P))==0break;endend應(yīng)用：f=imread('weld.tif');figure(1),imshow(f)g=regiongrow3(f,30);figure(2),imshow(g)代碼2：(質(zhì)心型)function[a]=regiongrow4(f,T)imshow(f);se=[0,1,0;1,1,1;0,1,0];[y,*]=ginput();*=floor(*);y=floor(y);a=im2bw(zeros(size(f)));a(*,y)=1;average=f(*,y);while1b=imdilate(a,se);P=b-a;inde*=find(P);n=size(inde*);fori=1:nt=abs(average-double(f(inde*(i))));ift>=Tb(inde*(i))=0;P(inde*(i))=0;endenda=b;inde*b=find(b);N=length(inde*b);All=0;fori=1:NAll=All+double(f(inde*b(i)));endaverage=All/N;ifsum(sum(P))==0break;endend結(jié)果：用課本中分水嶺的距離變換分水嶺分割、梯度分水嶺分割和控制標(biāo)記符分水嶺分割對(duì)liver1和liver2圖像進(jìn)行分割，分析上述算法的特點(diǎn)和存在的問(wèn)題，考慮如何進(jìn)行改進(jìn)？代碼1：closeall;f=imread('liver1.bmp');figure(1),imshow(f);%g=im2bw(f,graythresh(f));[g,NR]=regiongrow(f,255,135);%肝臟分離figure(2),imshow(g);gc=~g;D=bwdist(gc);L=watershed(-D);w=L==0;figure(3),imshow(w);g2=g&~w;figure(5),imshow(g2);代碼2：closeall;f=imread('liver1.bmp');F=regiongrow(f,255,135);%F=im2bw(f,graythresh(f));figure(1),imshow(F);h=fspecial('sobel');fd=double(f);g=sqrt(imfilter(fd,h,'replicate').^2+...imfilter(fd,h','replicate').^2);figure(2),imshow(g);L=watershed(g);wr=L==0;figure(3),imshow(wr);g2=imclose(imopen(g,ones(3,3)),ones(3,3));L2=watershed(g2);wr2=L2==0;f2=F;f2(wr2)=255;figure(4),imshow(f2);代碼3：closeall;f=imread('liver1.bmp');F=regiongrow(f,255,135);%F=im2bw(f,graythresh(f));figure(1),imshow(F);h=fspecial('sobel');fd=double(f);g=sqrt(imfilter(fd,h,'replicate').^2+...imfilter(fd,h','replicate').^2);figure(2),imshow(g);L=watershed(g);wr=L==0;figure(3),imshow(wr);rm=imregionalmin(g);im=ime*tendedmin(f,2);fim=f;fim(im)=175;Lim=watershed(bwdist(im));em=Lim==0;figure(4),imshow(em);g2=imimposemin(g,im|em);L2=watershed(g2);f2=f;f2(L2==0)=255;figure(5),imshow(f2);實(shí)驗(yàn)報(bào)告：實(shí)驗(yàn)?zāi)康膶?shí)驗(yàn)容（部分可略寫）每題分開寫，并標(biāo)明題號(hào)實(shí)驗(yàn)小結(jié)和體會(huì)小結(jié)：第一題重在理解S和T的涵義，若S是一個(gè)標(biāo)量，則它定義一個(gè)亮度值，f中有著該值的所有點(diǎn)都變成種子；若T是標(biāo)量，則它會(huì)定義一個(gè)全局閾值，閾值用來(lái)測(cè)試圖像中的像素是否與該種子或8連接