【CDBS】凹边界的修改形状特征描述：应用于皮肤病变分类（Matlab代码实现）

% compute Convex Deficiency-Based Signature for an examplary melanoma lesion
% exctracting 5 proposed features from CDBS curve
% exctracting 12 shape & textural features
% plotting CDBS curve
% detecting type of the lesion
clc
clear all
close all
% reading original melanoma image & its binary mask
I=imread('./SSM25_orig.jpg');
imBW=imread('./SSM25_contour.png');
% Preprocessing the input RGB image
im = PREPROCESS(I);
% obtaining coordinates of lesion contour
[B,L] = bwboundaries(imBW,'noholes');
bn=B{1,1};
% obtaining convex hull of lesion shape
s1=regionprops(L,'ConvexHull','Area','Perimeter','centroid',...
    'PixelList','Solidity','Extent','BoundingBox');
ch=cat(1,s1.ConvexHull);
% calculating CDBS
di=zeros(length(ch),length(bn));
for j=1:length(bn)
    for k=1:length(ch)
        di(k,j)=sqrt( (bn(j,1)-ch(k,2))^2+(bn(j,2)-ch(k,1))^2 );
    end  
end
CDBS=min(di);
%% Extracting features from CDBS Curve
% Finding Local Extrema
[ymax1,imax1,ymin1,imin1] = extrema(CDBS);
% Variance of CDBS curve
Fp(1,1) = var(CDBS,1);
% 10th order central standardized moment of CDBS curve
Fp(2,1) = normoment(CDBS,10);
% Ratio of Median point of CDBS curve 
% to number of boundary points
Fp(3,1) = median(CDBS)/length(CDBS);
% Ratio of number of zero-incidence points on 
% CDBS curve to number of boundary points
Fp(4,1) = (length(find(CDBS==min(CDBS))))/length(CDBS);
% Ratio of number of zero-incidence points to the   
% number of maximum extrema on CDBS curve
Fp(5,1) = (length(find(CDBS==min(CDBS))))/length(ymax1);
%% Extracting shape & textural features
% Transforming main image to gray scale
imgray=rgb2gray(im);
% Transforming main image to HSV color space
imhsv=rgb2hsv(im);
% lesion area
A=cat(1,s1.Area);
% lesion perimeter
P=cat(1,s1.Perimeter);
% coordinate of lesion center
centroids=cat(1,s1.Centroid);
% bounding box information
Box=cat(1,s1.BoundingBox);
% coordinates of lesion pixels
Pix_list=cat(1,s1.PixelList);
% obtaining lesion Major axis & Minor axis information
s2=diameter(L);
maj_co=s2.MajorAxis;
min_co=s2.MinorAxis;
% Computing Major Axis Length 
max_axis=sqrt((maj_co(1,1)-maj_co(2,1))^2+...
    (maj_co(1,2)-maj_co(2,2))^2);
% Computing Minor Axis Length
min_axis=sqrt((min_co(1,1)-min_co(2,1))^2+...
    (min_co(1,2)-min_co(2,2))^2);
% Computing distance of left corner of bounding box
% from centroid of the lesion
dist_Cbox=((Box(1,1)-centroids(1,1))^2+...
    (Box(1,2)-centroids(1,2))^2);
% Computing sum of entire gray scale values of lesion 
for j=1:length(Pix_list)
    gray1(j,1)=imgray(Pix_list(j,2),Pix_list(j,1));
end
% Computing sum of all lesion region values in HSV color space
for k=1:3
    for j=1:length(Pix_list)
        hsv1(j,k)=imhsv(Pix_list(j,2),Pix_list(j,1),k);
    end
    Shsv(1,k)=sum(hsv1(:,k));
end
% Ratio of lesion area to its bounding box area.
Fl(1,1)=cat(1,s1.Extent);
% Ratio of maximum axis of the lesion to its minimum axis
Fl(2,1)=max_axis/min_axis;
% Ratio of the lesion bounding box length to its bounding box width
Fl(3,1)=Box(1,3)/Box(1,4);
% Ratio of lesion area to its convex hull area
Fl(4,1)=cat(1,s1.Solidity);
% Ratio of the lesion area to the distance of leftcorner
% of bounding box from centroid of the lesion
Fl(5,1)=A/dist_Cbox;
% Ratio of sum of entire gray scale values of
% lesion region to its area 
Fl(6,1)=sum(gray1)/A;
% Ratio of the lesion area to its squared perimeter
Fl(7,1)=A/(P^2);
% Ratio of the lesion perimeter to its bounding box perimater
Fl(8,1)=P/(2*(Box(1,3) + Box(1,4)));
% Ratio of the sum of all lesion region values 
% in HSV color space to the lesion area
Fl(9,1)=Shsv(1,1)/A;
Fl(10,1)=Shsv(1,2)/A;
Fl(11,1)=Shsv(1,3)/A;
% A nonlinear combination of Fl(6,1) & Fl(11,1)
Fl(12,1) = Fl(6,1)^2 + Fl(11,1)^2;
%% figures 
% displaying melanoma lesion with its convex hull
figure(1),
plot(bn(:,2),bn(:,1),'Color','k','LineWidth',2)
hold on
plot(ch(:,1),ch(:,2),'Color','k',...
    'LineWidth',3,'LineStyle','--')
legend('C(t)','H(l)')
hold off
saveas(gcf, './imBW.png')
% plotting CDBS curve of a melanoma lesion
figure(2),plot(CDBS,'Color','b','LineWidth',3)
grid on
xlabel('t','FontSize',10,'FontWeight','bold')
ylabel('RCH(t)','FontSize',10,'FontWeight','bold')
title('CDBS Curve')
saveas(gcf, './CDBScurve.png')
%% Estimating type of lesion
% combining Fp & Fl
F = [Fl; Fp];
% loading data: weight matrices, randomly split trainset, 
% testset and their targets
load('./Preset.mat')
% loading normalizing factors of each fold
load('./mMax.mat')
M = 0;
nM = 0;
% detecting types of the lesion by performing the classification 
% algorithm over 10 separate trials
for kk = 1:10
    % loading weight matrices for each trials
    Pre = Preset{kk,1};
    Weight = Pre{1,3};
    % 
    minmax1 = mMax{kk,1};
    % calculating the output of sample during 5-fold cross-validation
    for gg = 1:5
        % min features
        min_f = minmax1{gg,1};
        % max features
        max_f = minmax1{gg,2};
        % Normalizing Test data
        Testdata = (F'-min_f)./(max_f-min_f);
        % loading wieght matrix of first layer in each trial
        v = Weight{gg,2};
        % loading wieght matrix of second layer in each trial
        w = Weight{gg,1};
        % compute output for testset
        y = Output(Testdata,v,w);
        % counting the number of times that test data is detected as 
        % melanoma or non-melanoma in each fold and each trial
        if y(1,1)>y(2,1)
            M = M + 1;
        else 
            nM = nM + 1;
        end
        w=[];v=[];
    end
    Pre = []; minmax1 = [];
end
% detecting the type of the lesion by majority voting
if M > nM
    display('Melanoma Detected')
else 
    display('non-Melanoma Detected')
end