5.1 Feature extraction and classification

Automatic white blood cell Page 1 / 1


Svm method
  % Performs classification of white blood cell (WBC) types
% SVM classification (1v1 and 1vAll) using the features homogenity,% contrast, entropy, and compactness

function wbc_SVM% =========================== Load Data =============================== %

% Load all images: training, cross-validation, and test sets[I_c_train, I_n_train, I_bin_c_train, I_bin_n_train, Y_train, m_train] = load_data(1,18);[I_c_cv, I_n_cv, I_bin_c_cv, I_bin_n_cv, Y_cv, m_cv] = load_data(19,24);[I_c_test, I_n_test, I_bin_c_test, I_bin_n_test, Y_test, m_test] = load_data(25,30);% ======================== Feature Extraction ===================== %

% Calculate cell to nucleus area ratio, cell perimeter ratio, nucleus% perimeter ratio, nucleus circularity and cell circularity
[fMat_train, A_c_train, A_n_train, ~, P_n_train]= binFeatures(I_bin_c_train, I_bin_n_train);
[fMat_cv, A_c_cv, A_n_cv, ~, P_n_cv]= binFeatures(I_bin_c_cv, I_bin_n_cv);
[fMat_test, A_c_test, A_n_test, ~, P_n_test]= binFeatures(I_bin_c_test, I_bin_n_test);

% Calculate homogeneity, contrast, and entropy[H_c_train,cst_c_train,E_c_train] = gray(I_c_train,A_c_train);[H_n_train,cst_n_train,E_n_train] = gray(I_n_train,A_n_train);[H_c_cv,cst_c_cv,E_c_cv] = gray(I_c_cv,A_c_cv);[H_n_cv,cst_n_cv,E_n_cv] = gray(I_n_cv,A_n_cv);[H_c_test,cst_c_test,E_c_test] = gray(I_c_test,A_c_test);[H_n_test,cst_n_test,E_n_test] = gray(I_n_test,A_n_test);% Calculate compactness of nuclei
cmp_train = compact(A_n_train,P_n_train);cmp_cv = compact(A_n_cv,P_n_cv);
cmp_test = compact(A_n_test,P_n_test);% create feature matrix
X_pre_train = [H_c_train; H_n_train; cst_c_train; cst_n_train; E_c_train; E_n_train; cmp_train; fMat_train]';
% m_train by N matrix: m_train = total number of training examples, N = number of featuresX_pre_cv = [H_c_cv; H_n_cv; cst_c_cv; cst_n_cv; E_c_cv; E_n_cv; cmp_cv; fMat_cv]';% m_cv by N matrix: m_cv = total number of cross-validation examples, N = number of features
X_pre_test = [H_c_test; H_n_test; cst_c_test; cst_n_test; E_c_test; E_n_test; cmp_test; fMat_test]';
% m_test by N matrix: m_test = total number of test examples, N = number of features% normalize feature matricies
X_train = normalize(X_pre_train);X_cv = normalize(X_pre_cv);
X_test = normalize(X_pre_test);% =============== Train SVM and Perform Cross-validation =============== %

[model_1v1, model_1vAll]= crossval(X_train,Y_train,X_cv,Y_cv);


% ==================== Apply SVM Model to Test Data =================== %% ------------------- One vs One ---------------- %

[pred3, acc3, ~]= svmpredict(Y_test, X_test, model_1v1);

% ------------------- One vs All ---------------- %[pred4, acc4, ~] = ovrpredict(Y_test, X_test, model_1vAll);% report results of classification
fprintf('Classification Complete!\n\n')fprintf('Accuracy on Test Set: 1v1 = %f, 1vAll = %f\n',acc3(1),acc4(1)*100)
fprintf('\nPredictions:\n\n')for i = 1:length(pred3)
    fprintf('Actual: %f, Predicted by 1v1: %f, Predicted by 1vAll: %f\n',Y_test(i),pred3(i),pred4(i))end

% ==================== Print Feature Statistics ========================%fprintf('Feature Statistics for Training Data:\n\n')
feature_stats(X_train,m_train);fprintf('Feature Statistics for Cross-Validation Data:\n\n')
feature_stats(X_cv,m_cv);fprintf('Feature Statistics for Test Data:\n\n')
feature_stats(X_test,m_test);end

% gray calculates homogeneity, normalized contrast, and normalized% entropy of a grayscale image
% H = homogenity% cst = normalized contrast
% E = normalized entropyfunction [H,cst,E] = gray(I,A)H = zeros(1,length(A));
cst = zeros(1,length(A));E = zeros(1,length(A));
for i = 1:length(A)% Calculate grayscale co-occurance matrix
    glcm = graycomatrix(I(1+250*(i-1):250+250*(i-1),:));% Calculate homogeneity
    H_temp = graycoprops(glcm,'Homogeneity');H(i) = H_temp.Homogeneity;
    % Calculate contrast and normalize by areacst_temp = graycoprops(glcm,'Contrast');
    cst(i) = cst_temp.Contrast;% Calculate entropy and normalize by area
    E(i) = entropy(I(1+200*(i-1):200+200*(i-1),:));end
end% compact calculates compactness
% A = area of nucleus% P = perimeter of nucleus
% cmp = compactnessfunction cmp = compact(A,P)
for i = 1:length(A)% Calculate area of circle with the same perimeter as the nucleus
    r = P(i)/(2*pi);circ_A = pi*r^2;
    % Calculate compactnesscmp(i) = sqrt(A(i)/circ_A);
endend

% ovrtrain and ovrperdict from LibSVM multiclass implementation% Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector 
% machines. ACM Transactions on Intelligent Systems and Technology,% 2:27:1--27:27, 2011. Software available at 
% http://www.csie.ntu.edu.tw/~cjlin/libsvmfunction [model] = ovrtrain(y, x, cmd)labelSet = unique(y);
labelSetSize = length(labelSet);models = cell(labelSetSize,1);

for i=1:labelSetSizemodels{i} = svmtrain(double(y == labelSet(i)), x, cmd);
endmodel = struct('models', {models}, 'labelSet', labelSet);
end% ovrtrain and ovrperdict from LibSVM multiclass implementation
% Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector% machines. ACM Transactions on Intelligent Systems and Technology, 
% 2:27:1--27:27, 2011. Software available at% http://www.csie.ntu.edu.tw/~cjlin/libsvm
function [pred, ac, decv]= ovrpredict(y, x, model)

labelSet = model.labelSet;labelSetSize = length(labelSet);
models = model.models;decv= zeros(size(y, 1), labelSetSize);

for i=1:labelSetSize[l,a,d] = svmpredict(double(y == labelSet(i)), x, models{i});decv(:, i) = d * (2 * models{i}.Label(1) - 1);
end[tmp,pred] = max(decv, [], 2);
pred = labelSet(pred);ac = sum(y==pred) / size(x, 1);
end% load cell images
function [I_c, I_n, I_bin_c, I_bin_n, Y, m]= load_data(start,stop)

% =================== Load cell and nuclei images ===================== %folder = 'C:\Users\Megan Kehoe\Documents\Junior Year\ELEC 301\grayscale_images\';
celltype = {'neutrophil''monocyte'
    'lymphocyte''eosinophil'
    'basophil'};I_c = zeros((stop-start+1)*5*250,250);
I_n = zeros((stop-start+1)*5*250,250);
iter = 1;for j = 1:5
    for i = start:stop% filename of relevant pictures
        pic_gray_cell = strcat(folder, 'gray\', celltype{j}, num2str(i), '_cellgray.bmp');im_gray_cell = imread(pic_gray_cell);
        pic_gray_nuc = strcat(folder, 'gray\', celltype{j}, num2str(i), '_nucleusgray.bmp');im_gray_nuc = imread(pic_gray_nuc);
        pic_bin_cell = strcat(folder, 'binary\', celltype{j}, num2str(i), '_cellcrop.bmp');im_bin_cell = imread(pic_bin_cell);
        pic_bin_nuc = strcat(folder, 'binary\', celltype{j}, num2str(i), '_nucleuscrop.bmp');im_bin_nuc = imread(pic_bin_nuc);
        % matrix for grayscale cell imagesI_c(iter*250-249:iter*250,:) = im_gray_cell;
        % matrix for grayscale nuclei imagesI_n(iter*250-249:iter*250,:) = im_gray_nuc;
        % matrix for binary cell imagesI_bin_c(iter*250-249:iter*250,:) = im_bin_cell;
        % matrix for binary nuclei imagesI_bin_n(iter*250-249:iter*250,:) = im_bin_nuc;
        iter = iter+1;end
end% =================== Create label matrix ===================== %

% Y is Nx1 matrix: N = number of training examples% 1 = basophil
% 2 = eosinophil% 3 = lymphocyte
% 4 = monocyte% 5 = neutrophil
m = stop-start+1; % number of training examples per cell typeY_bas = ones(m,1);
Y_eos = 2*ones(m,1);Y_lym = 3*ones(m,1);
Y_mon = 4*ones(m,1);Y_neu = 5*ones(m,1);
Y = [Y_bas; Y_eos; Y_lym; Y_mon; Y_neu];
end% print mean and standard deviation of features for each WBC type
function feature_stats(X,m)% section feature vector into WBC types
X_bas = X(1:m,:);X_eos = X(1+m:2*m,:);
X_lym = X(1+2*m:3*m,:);X_mon = X(1+3*m:4*m,:);
X_neu = X(1+4*m:5*m,:);% calculate mean of normalized features for each WBC type
mean_bas = mean(X_bas);mean_eos = mean(X_eos);
mean_lym = mean(X_lym);mean_mon = mean(X_mon);
mean_neu = mean(X_neu);% calculate std dev of normalized features for each WBC type
std_bas = std(X_bas);std_eos = std(X_eos);
std_lym = std(X_lym);std_mon = std(X_mon);
std_neu = std(X_neu);fprintf('Mean of Normalized Features\n\n')
fprintf('WBC type    |    H_c     |    H_n     |    cst_c   |    cst_n   |')fprintf('     E_c    |    E_n     |    cmp     |    aRatio  |  pRatio_c  |')
fprintf('  pRatio_n  |  circ_n    |   circ_c\n')fprintf('Basophil    |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f\n',...
    mean_bas(1),mean_bas(2),mean_bas(3),mean_bas(4),mean_bas(5), mean_bas(6),...mean_bas(7), mean_bas(8), mean_bas(9), mean_bas(10), mean_bas(11), mean_bas(12))
fprintf('Eosinophil  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f\n',...mean_eos(1),mean_eos(2),mean_eos(3),mean_eos(4),mean_eos(5), mean_eos(6),...
    mean_eos(7), mean_eos(8), mean_eos(9), mean_eos(10), mean_eos(11), mean_eos(12))fprintf('Lymphocyte  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f\n',...
    mean_lym(1),mean_lym(2),mean_lym(3),mean_lym(4),mean_lym(5), mean_lym(6),...mean_lym(7), mean_lym(8), mean_lym(9), mean_lym(10), mean_lym(11), mean_lym(12))
fprintf('Monocyte    |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f\n',...mean_mon(1),mean_mon(2),mean_mon(3),mean_mon(4),mean_mon(5), mean_mon(6),...
    mean_mon(7), mean_mon(8), mean_mon(9), mean_mon(10), mean_mon(11), mean_mon(12))fprintf('Neutrophil  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f\n\n',...
    mean_neu(1),mean_neu(2),mean_neu(3),mean_neu(4),mean_neu(5), mean_neu(6),...mean_neu(7), mean_neu(8), mean_neu(9), mean_neu(10), mean_neu(11), mean_neu(12))

fprintf('Standard Deviation of Normalized Features\n\n')fprintf('WBC type    |    H_c     |    H_n     |    cst_c   |    cst_n   |')
fprintf('     E_c    |    E_n     |    cmp     |    aRatio  |  pRatio_c  |')fprintf('  pRatio_n  |  circ_n    |   circ_c\n')
fprintf('Basophil    |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f\n',...std_bas(1),std_bas(2),std_bas(3),std_bas(4),std_bas(5), std_bas(6),...
    std_bas(7), std_bas(8), std_bas(9), std_bas(10), std_bas(11), std_bas(12))fprintf('Eosinophil  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f\n',...
    std_eos(1),std_eos(2),std_eos(3),std_eos(4),std_eos(5), std_eos(6),...std_eos(7), std_eos(8), std_eos(9), std_eos(10), std_eos(11), std_eos(12))
fprintf('Lymphocyte  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f\n',...std_lym(1),std_lym(2),std_lym(3),std_lym(4),std_lym(5), std_lym(6),...
    std_lym(7), std_lym(8), std_lym(9), std_lym(10), std_lym(11), std_lym(12))fprintf('Monocyte    |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f\n',...
    std_mon(1),std_mon(2),std_mon(3),std_mon(4),std_mon(5), std_mon(6),...std_mon(7), std_mon(8), std_mon(9), std_mon(10), std_mon(11), std_mon(12))
fprintf('Neutrophil  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f  |  %f\n\n',...std_neu(1),std_neu(2),std_neu(3),std_neu(4),std_neu(5), std_neu(6),...
    std_neu(7), std_neu(8), std_neu(9), std_neu(10), std_neu(11), std_neu(12))end

% train SVM and perform cross-validation to select SM parametersfunction [model_1v1, model_1vAll] = crossval(X_train, Y_train, X_cv, Y_cv)iter = 1;
for C = [0.01 0.1 1 5 10 25 50]for gamma = [0.0002 0.02 0.2 2 5 10]% set parameters
        % -s 0 : C-SVC (C-paramaterized SVM)% -t 2 : Gaussian kernel
        % -g : value of gamma parameter% -c : value of C parameter
        par_train = ['-s 0 -t 2 -b 1 -g ', num2str(gamma), ' -c ', num2str(C)];% ------------------- One vs One ---------------- %% train the model
        model = svmtrain(Y_train, X_train, par_train);% apply SVM model to cross-validation data% inclusion of Y_cv is for calculation of accuracy
        [~, acc, ~]= svmpredict(Y_cv, X_cv, model);

        % --------------- One vs All SVM --------------- %% train the modelmodel2 = ovrtrain(Y_train, X_train, par_train);% apply SVM model to cross-validation data
        % inclusion of Y_cv is for calculation of accuracy[~, acc2, ~] = ovrpredict(Y_cv, X_cv, model2);% ----------- Store info for each combo of C, gamma ------------ %% store accuracy for each comboacc_1v1(iter,:) = [acc(1) C gamma];acc_1vAll(iter,:) = [acc2(1) C gamma];% store model for each combomodel_1v1(iter) = model;
        model_1vAll(iter) = model2;iter = iter+1;end
end% select parameters for 1v1 all with highest accuracy
[A_1v1,I_1v1]= max(acc_1v1(:,1));
C_1v1 = acc_1v1(I_1v1,2);gamma_1v1 = acc_1v1(I_1v1,3);
model_1v1 = model_1v1(I_1v1);% select parameters for 1vAll all with highest accuracy
[A_1vAll,I_1vAll]= max(acc_1vAll(:,1));
C_1vAll = acc_1vAll(I_1vAll,2);gamma_1vAll = acc_1vAll(I_1vAll,3);
model_1vAll = model_1vAll(I_1vAll);% report results of cross validation
fprintf('Cross Validation Complete!\n\n')fprintf('Accuracy on Cross Validation Set: 1v1 = %f, 1vAll = %f\n',A_1v1,A_1vAll*100)
fprintf('Optimized Parameters for 1v1:\n')fprintf('C = %f | gamma = %f\n------\n',C_1v1,gamma_1v1)
fprintf('Optimized Parameters for 1vAll:\n')fprintf('C = %f | gamma = %f\n------\n\n',C_1vAll,gamma_1vAll)
end% normalize feature vectors by subtracting mean and dividing by standard
% deviationfunction X = normalize(X_pre)
X_mean = mean(X_pre);X_std = std(X_pre);

X = zeros(size(X_pre));for i = 1:length(X_mean)
    X(:,i) = (X_pre(:,i) - X_mean(i)) / X_std(i);end
end% calculate cell to nucleus area ratio, cell perimeter ratio, nucleus
% perimeter ratio, nucleus circularity and cell circularityfunction [fMat, aCell, aNuc, pCell, pNuc] = binFeatures(I_c, I_n)N = length(I_c)/250;
fMat = zeros(5,N);for i = 1:N
    imgCell = I_c(1+250*(i-1):250+250*(i-1),:);imgNuc = I_n(1+250*(i-1):250+250*(i-1),:);aNuc(i) = sum(sum(imgNuc));                    % find area of cellaCell(i) = sum(sum(imgCell));
    aRatio = aNuc(i)/aCell(i);pCell_temp = regionprops(imgCell, 'perimeter');  % get perimeterpCell(i) = sum(cat(1,pCell_temp.Perimeter));        % sum all the perimeters
    pCellRatio = aCell(i)/pCell(i);                   % find ratio of area to perimeterpNuc_temp = regionprops(imgNuc, 'perimeter');pNuc(i) = sum(cat(1,pNuc_temp.Perimeter));
    pNucRatio = aNuc(i)/pNuc(i);                   % find ratio of area to perimetercCell = 4*pi*aCell(i)/(pCell(i)^2);cNuc = 4*pi*aNuc(i)/(pNuc(i)^2);fMat(1,i) = aRatio;
    fMat(2,i) = pCellRatio;fMat(3,i) = pNucRatio;
    fMat(4,i) = cNuc;fMat(5,i) = cCell;
endend


Neural network method
  % Performs classification of white blood cell (WBC) types
% Neural Network using binary and grayscale featuresfunction wbc_NN

% =========================== Load Data =============================== %% Load all images: training, cross-validation, and test sets
[I_c_train, I_n_train, I_bin_c_train, I_bin_n_train]= load_data(1,18);
[I_c_cv, I_n_cv, I_bin_c_cv, I_bin_n_cv]= load_data(19,24);
[I_c_test, I_n_test, I_bin_c_test, I_bin_n_test]= load_data(25,30);

% ======================== Feature Extraction ===================== %% Calculate cell to nucleus area ratio, cell perimeter ratio, nucleus
% perimeter ratio, nucleus circularity and cell circularity[fMat_train, A_c_train, A_n_train, ~, P_n_train] = binFeatures(I_bin_c_train, I_bin_n_train);[fMat_cv, A_c_cv, A_n_cv, ~, P_n_cv] = binFeatures(I_bin_c_cv, I_bin_n_cv);[fMat_test, A_c_test, A_n_test, ~, P_n_test] = binFeatures(I_bin_c_test, I_bin_n_test);% Calculate homogeneity, contrast, and entropy
[H_c_train,cst_c_train,E_c_train]= gray(I_c_train,A_c_train);
[H_n_train,cst_n_train,E_n_train]= gray(I_n_train,A_n_train);
[H_c_cv,cst_c_cv,E_c_cv]= gray(I_c_cv,A_c_cv);
[H_n_cv,cst_n_cv,E_n_cv]= gray(I_n_cv,A_n_cv);
[H_c_test,cst_c_test,E_c_test]= gray(I_c_test,A_c_test);
[H_n_test,cst_n_test,E_n_test]= gray(I_n_test,A_n_test);

% Calculate compactness of nucleicmp_train = compact(A_n_train,P_n_train);
cmp_cv = compact(A_n_cv,P_n_cv);cmp_test = compact(A_n_test,P_n_test);

% create feature matrixX_pre_train = [H_c_train; H_n_train; cst_c_train; cst_n_train; E_c_train; E_n_train; cmp_train; fMat_train]';% m_train by N matrix: m_train = total number of training examples, N = number of features
X_pre_cv = [H_c_cv; H_n_cv; cst_c_cv; cst_n_cv; E_c_cv; E_n_cv; cmp_cv; fMat_cv]';
% m_cv by N matrix: m_cv = total number of cross-validation examples, N = number of featuresX_pre_test = [H_c_test; H_n_test; cst_c_test; cst_n_test; E_c_test; E_n_test; cmp_test; fMat_test]';% m_test by N matrix: m_test = total number of test examples, N = number of features

% normalize feature matriciesX_train = normalize(X_pre_train);
X_cv = normalize(X_pre_cv);X_test = normalize(X_pre_test);

X_fin = [X_train; X_cv; X_test];
assignin('base','X_fin',X_fin)% =================== Create label matrix ===================== %

Y_fin = zeros(150,5);Y_fin(1:30,1) = 1;
Y_fin(31:60,2) = 1;Y_fin(61:90,3) = 1;
Y_fin(91:120,4) = 1;Y_fin(121:150,5) = 1;
assignin('base','Y_fin',Y_fin)% =================== nprtool generated function ===================== %

[Y]= WBCNN(X_fin');

display(Y)end

function [y1]= WBCNN(x1)
%MYNEURALNETWORKFUNCTION neural network simulation function.%
% Generated by Neural Network Toolbox function genFunction, 16-Dec-2015 09:49:02.% 
% [y1]= myNeuralNetworkFunction(x1) takes these arguments:
%   x = 12xQ matrix, input #1% and returns:
%   y = 5xQ matrix, output #1% where Q is the number of samples.


  % ===== NEURAL NETWORK CONSTANTS =====% Input 1x1_step1_xoffset = [-5.90344272917747;-5.89208546208485;-0.637768770118539;-0.696293796788186;-3.40434320810666;-3.14312328174107;-2.39410595135809;-2.01864130150546;-3.1910660784115;-2.14939014482332;-1.89718479918728;-3.05498352388176];x1_step1_gain = [0.305753758338993;0.303564795136293;0.305753758338992;0.303564795136293;0.456766529694895;0.425301883679229;0.273588869713745;0.476586286231742;0.372673738909549;0.38842100799287;0.281202422061574;0.454059015183198];x1_step1_ymin = -1;% Layer 1
  b1 = [-1.7827809188065437684;1.4501033367829965215;1.5614509446982758334;-1.1677666592981630345;-1.0971408017647292787;0.82666557394503048517;-0.73702722139244292165;-0.26925862705950714959;0.21396444869375327857;-0.12468221414012239934;-0.022860822221021068895;-0.20125652475303329458;-0.74325367673251629963;0.38713637297072756027;-0.84801449750653035142;-0.97823549375533169759;-1.2790256982622778548;1.5202698097087843365;-1.6216552412380706993;-1.7883784278689605074];
  IW1_1 = [0.65269077321264334923 -0.62784813315786813792 0.23054673611401493849 -0.33438735781436157435 0.68915792596196057129 0.53946597872735746471 -0.54648719331940187605 0.65272674102503325244 -0.36900742533633940079 -0.33848182698405809843 -0.56173891213039706116 -0.39628871173455432197;-0.62425937074576121066 -0.86594909820268195499 0.96125420124238880604 0.86590158288784957463 -0.29305010408331222393 0.0088109667527298402012 -0.28116999609488529943 0.75223677521530230017 -0.49209950914243028031 0.22767237688696609355 0.28951181656027502598 -0.19858693413931510774;-0.87409457016809355423 -0.24176614380422495332 -0.59413860085417935508 -0.094415210593033371822 -0.23360535537645860105 0.68608568970845018598 -0.88215441881148537462 0.57376293215351292787 0.068425980303622582768 0.028422510155456427033 -0.10071770795761256223 -0.24446620334346177983;0.18317852639240186918 -0.25530896691186200087 0.1841456125704462321 0.6103504051006201081 -0.013016737208259132608 -0.68775458913187581533 -0.14767459853548081661 -0.74805029077191831366 0.37082888823626475316 0.68195761173755509166 -0.65453516994025384701 0.83748308940240767662;0.21219648095654405906 0.90461967533898235416 0.84212095721261492898 -0.54384105597011189737 0.26278653496445081306 0.22920001512820070699 -0.31635285349133523969 -0.2197292030955285691 0.79548984140271628007 0.27811838368491137441 -0.038988735226641604847 -0.61792904286326399976;-0.15995409068203175762 0.35192857507101771297 0.091572642749931237871 -0.58692351450684465686 0.71038167671771246248 -0.80275756583490087692 0.26769243284641780489 -0.10045427244525970134 -0.30783390052233811085 0.093758356605187886945 0.74312923232637795312 -0.83367174118791709247;0.17757047927313387992 0.29535786067408831279 -0.40086163424319443127 -0.79378179629056244693 0.7673384089879279335 -0.64048836337807124597 -0.55675213496442632621 -0.32416623027720903316 -0.67939842147653006776 0.26275724315411352894 0.21232423500480238077 0.31101061322804712805;0.6332800200766275367 0.93283001155606748434 -0.019359681845131680028 -0.057655482752824900594 0.010114559415013726906 -0.13946923636052924178 0.22558679233771050665 1.0942840616738851445 -0.11136271505772811496 -0.73253325042058281547 0.32294905645266119842 -0.52449976561828137722;-0.14073087220256236263 0.021212015547970253238 0.057140735770108572189 -0.058672300187391002169 0.52983578526836394573 -0.74887138757099414033 -0.57288741357302119805 -0.6283420285347269818 -0.52617375349461059653 -0.86951465293454088812 0.80919201098596682531 -0.32745155545458048962;0.17711366901026434628 -0.3415249419935150188 -0.80508835806010381475 -0.23379863144509765993 -0.42010221638258010701 -0.20053181629465288704 -0.95539500926782872092 -0.81885426378697545591 0.44341612216086750964 0.65430640353779834228 0.1842843931796633028 0.35512039383732951769;-0.3051127379722994859 0.62132057240537752474 0.26115351724392993349 0.65014762006875670419 -0.81614974961696440392 -0.14573608764669954141 0.82713955354982193757 0.78882592794118477908 -0.28590689427630500141 -0.26129213000622647511 -0.02020561330510827272 0.3373254660593946741;-0.77637198034006160707 -0.53951123217202268112 -0.46415082703041404821 0.031394647379238131701 -0.36615481448419945343 0.39064463087294948318 0.051552799417610144228 -0.52617430654106644994 0.53349297850098487128 -0.66128543196876077293 -0.015933919392453241293 0.49312948757777663733;-0.40109170760184048588 -0.80073843659735322031 -0.43930883908024825901 0.52174450165408969848 -0.51563427416045870544 0.05899251331655103181 -0.10723127387783933095 0.79323791479436156493 -0.19932362274186257722 0.84877089669146399409 0.34396274237390433992 0.067153874094595056299;-0.14966260299231101683 0.043414764816473203068 0.28029588061563043278 0.97473404212223824228 -0.017542187476933816109 -0.50697210513255575037 -0.57889211246397831445 0.25065675293005146296 0.50367137065966816056 -0.90358818880026914311 0.60437309508048953077 0.46220676188375653393;-0.62287679623497327341 -0.40452643027298235134 0.52128655366403209293 0.5343793144254529448 -0.033631083527157080992 -0.77501205700901520945 0.08631051496969871506 0.78963365219209102897 -0.45973983902683918101 -0.73557456159983314326 0.16790128002690762887 0.30544370296543016385;-0.64570433840792385016 -0.52544096066776668774 0.32899182851515956783 -0.74564190480302972031 0.37823110105658813707 -0.79582818968853852315 -0.62184971625171903131 0.55183313390807819943 0.48061153088261304722 0.1937955780359462421 0.33401686141670228203 0.45761760559864517184;-0.48398577866872094511 -0.51247727709974244537 0.014671039167520393692 1.0557973713321149312 -0.29689953549894754214 -0.65761764293316926633 -0.5810878874447493736 0.19700317375217216154 0.79378864664069426205 -0.14929342020070573982 -0.12689300872608549886 0.042426282965546707748;0.31817673028468090868 -0.25581136662340991927 0.53720337579369925596 0.53555993713253458033 0.54196787329454343407 -0.12164193332507920731 -0.58176293647635313189 0.68171724770757780032 -0.062736861681844752203 -0.7486116451177108333 0.18243184031698231329 -0.62307684852446698631;-0.069635276939243387351 -0.89084002756759428365 0.81166395697372595297 0.24423485016676374504 -0.42320893506046336485 0.18515385919973045836 -0.3076186239021012625 0.12357314038408856449 0.16305041020192123646 -0.80472851533031042326 -0.58188953694713496656 -0.56469246302677866822;-0.015588988418843111039 -0.52789681905975716081 -0.056374237729799464569 -0.89601894033758433533 0.39301007620720507241 -0.83670340441315416147 -0.30214624216503421783 -0.0057481349615575204684 -0.71307657083127085063 -0.48395831532568406308 0.69274963889038942977 -0.055266019413176742381];% Layer 2b2 = [0.53769303882958074947;-0.13217105139299178962;-0.041876094256106277669;-0.7675131872347213946;1.0087377770168073354];LW2_1 = [0.21962664914031176933 -0.72527984788570698527 0.48119075681324496863 -0.57920005507386096433 -0.49526572769559640275 -0.76883264351233959744 0.6077570552882199939 -0.65833342927171101255 0.46357354835090158751 0.16489556453274598069 -0.59151628713453463515 -0.26781130066266212175 0.87623321078000115936 0.72393553132938714967 -0.61151148862114468319 0.48388385514578952096 0.39865912449009027752 0.55897661778673335409 0.30969009674021441558 0.78045129153028458546;0.0035564839628999911185 -0.057628961929088326488 -0.088823889958358576147 -0.00064596083541778454018 0.055564189321075478645 0.15434573806520032746 0.78683235394159178888 -0.035924556214055346215 -0.67911040762302188334 -1.04974983100161956 0.33926375814301046319 -0.55803871691417183953 0.12403303585593739022 0.43037424532005968958 -0.69997975566461012598 -0.031919952989213386252 0.54694494666826709572 -0.081580997762416965213 -0.14448077219790639303 -0.92615947408013654663;0.24280165162834940751 -0.027248414507144408381 0.78376469520957747772 -0.45780077805308355687 -0.92600637679302721939 0.72254196979932472367 0.13582085498423490666 0.14902459081642380201 -0.85682223245604505202 0.10128386847234764623 0.61228308567052469602 0.17446679499524980761 0.27767541973378268017 -0.59222942240648046575 -0.35805815307864358177 0.93727112683396773818 -0.33110525022824094377 0.63339806688071265128 0.43718826827843576543 0.60407925200793333165;-0.11671612335179254449 0.76984021517165235338 0.76794893651558093772 0.73338157588525842989 -0.51980599903868773826 0.0042697073699095493618 0.07008170499484027427 0.43580004941712890965 -0.11825315186169747805 0.44134930378298420361 -0.33202715740492544372 -0.4975328211750704166 0.82433476791666115968 -0.30437588329739195814 -0.23548223964335837644 -0.5599252080618919436 -0.083567274004609434779 0.22438465533450058231 -0.91935923520596096736 0.61291406222756139588;0.53530034845386287312 0.98772941003983194541 -0.20305214990264566777 -0.65939248652009341267 0.28308058528562435319 -0.2875816696891681512 -0.028388049541314110125 0.49988032423210143618 -0.091158701345464332455 -0.11047284972129928216 -0.01324464759306478133 -0.84726415640066821133 -0.73598291099507062718 0.33689391765723658567 -0.78614323618766546176 0.86741256483480888573 -0.79467674930756371232 -0.31322875302042174628 0.96921512208426130464 0.39684880198380179106];% ===== SIMULATION ========% DimensionsQ = size(x1,2); % samples% Input 1
  xp1 = mapminmax_apply(x1,x1_step1_gain,x1_step1_xoffset,x1_step1_ymin);% Layer 1a1 = tansig_apply(repmat(b1,1,Q) + IW1_1*xp1);% Layer 2
  a2 = softmax_apply(repmat(b2,1,Q) + LW2_1*a1);% Output 1y1 = a2;
end% ===== MODULE FUNCTIONS ========

% Map Minimum and Maximum Input Processing Functionfunction y = mapminmax_apply(x,settings_gain,settings_xoffset,settings_ymin)
  y = bsxfun(@minus,x,settings_xoffset);y = bsxfun(@times,y,settings_gain);
  y = bsxfun(@plus,y,settings_ymin);end

% Competitive Soft Transfer Functionfunction a = softmax_apply(n)
  nmax = max(n,[],1);
  n = bsxfun(@minus,n,nmax);numer = exp(n);
  denom = sum(numer,1);denom(denom == 0) = 1;
  a = bsxfun(@rdivide,numer,denom);end

% Sigmoid Symmetric Transfer Functionfunction a = tansig_apply(n)
  a = 2 ./ (1 + exp(-2*n)) - 1;end

% gray calculates homogeneity, normalized contrast, and normalized% entropy of a grayscale image
% H = homogenity% cst = normalized contrast
% E = normalized entropyfunction [H,cst,E] = gray(I,A)H = zeros(1,length(A));
cst = zeros(1,length(A));E = zeros(1,length(A));
for i = 1:length(A)% Calculate grayscale co-occurance matrix
    glcm = graycomatrix(I(1+250*(i-1):250+250*(i-1),:));% Calculate homogeneity
    H_temp = graycoprops(glcm,'Homogeneity');H(i) = H_temp.Homogeneity;
    % Calculate contrast and normalize by areacst_temp = graycoprops(glcm,'Contrast');
    cst(i) = cst_temp.Contrast;% Calculate entropy and normalize by area
    E(i) = entropy(I(1+200*(i-1):200+200*(i-1),:));end
end% compact calculates compactness
% A = area of nucleus% P = perimeter of nucleus
% cmp = compactnessfunction cmp = compact(A,P)
for i = 1:length(A)% Calculate area of circle with the same perimeter as the nucleus
    r = P(i)/(2*pi);circ_A = pi*r^2;
    % Calculate compactnesscmp(i) = sqrt(A(i)/circ_A);
endend

% load cell imagesfunction [I_c, I_n, I_bin_c, I_bin_n] = load_data(start,stop)% =================== Load cell and nuclei images ===================== %

folder = '/Users/Yusi/Downloads/';celltype = {'neutrophil'
    'monocyte''lymphocyte'
    'eosinophil''basophil'};
I_c = zeros((stop-start+1)*5*250,250);I_n = zeros((stop-start+1)*5*250,250);
iter = 1;for j = 1:5
    for i = start:stop% filename of relevant pictures
        pic_gray_cell = strcat(folder, 'gray/', celltype{j}, num2str(i), '_cellgray.bmp');im_gray_cell = imread(pic_gray_cell);
        pic_gray_nuc = strcat(folder, 'gray/', celltype{j}, num2str(i), '_nucleusgray.bmp');im_gray_nuc = imread(pic_gray_nuc);
        pic_bin_cell = strcat(folder, 'binary/', celltype{j}, num2str(i), '_cellcrop.bmp');im_bin_cell = imread(pic_bin_cell);
        pic_bin_nuc = strcat(folder, 'binary/', celltype{j}, num2str(i), '_nucleuscrop.bmp');im_bin_nuc = imread(pic_bin_nuc);
        % matrix for grayscale cell imagesI_c(iter*250-249:iter*250,:) = im_gray_cell;
        % matrix for grayscale nuclei imagesI_n(iter*250-249:iter*250,:) = im_gray_nuc;
        % matrix for binary cell imagesI_bin_c(iter*250-249:iter*250,:) = im_bin_cell;
        % matrix for binary nuclei imagesI_bin_n(iter*250-249:iter*250,:) = im_bin_nuc;iter = iter+1;
    endend
end% normalize feature vectors by subtracting mean and dividing by standard
% deviationfunction X = normalize(X_pre)
X_mean = mean(X_pre);X_std = std(X_pre);

X = zeros(size(X_pre));for i = 1:length(X_mean)
    X(:,i) = (X_pre(:,i) - X_mean(i)) / X_std(i);end
end% calculate cell to nucleus area ratio, cell perimeter ratio, nucleus
% perimeter ratio, nucleus circularity and cell circularityfunction [fMat, aCell, aNuc, pCell, pNuc] = binFeatures(I_c, I_n)N = length(I_c)/250;
fMat = zeros(5,N);for i = 1:N
    imgCell = I_c(1+250*(i-1):250+250*(i-1),:);imgNuc = I_n(1+250*(i-1):250+250*(i-1),:);aNuc(i) = sum(sum(imgNuc));                    % find area of cell
    aCell(i) = sum(sum(imgCell));aRatio = aNuc(i)/aCell(i);pCell_temp = regionprops(imgCell, 'perimeter');  % get perimeter
    pCell(i) = sum(cat(1,pCell_temp.Perimeter));        % sum all the perimeterspCellRatio = aCell(i)/pCell(i);                   % find ratio of area to perimeterpNuc_temp = regionprops(imgNuc, 'perimeter');
    pNuc(i) = sum(cat(1,pNuc_temp.Perimeter));pNucRatio = aNuc(i)/pNuc(i);                   % find ratio of area to perimetercCell = 4*pi*aCell(i)/(pCell(i)^2);
    cNuc = 4*pi*aNuc(i)/(pNuc(i)^2);fMat(1,i) = aRatio;fMat(2,i) = pCellRatio;
    fMat(3,i) = pNucRatio;fMat(4,i) = cNuc;
    fMat(5,i) = cCell;end  
end

Questions & Answers

how do you get the 2/50

Abba Reply

number of sport play by 50 student construct discrete data

Aminu Reply

width of the frangebany leaves on how to write a introduction

Theresa Reply

Solve the mean of variance

Veronica Reply

Step 1: Find the mean. To find the mean, add up all the scores, then divide them by the number of scores. ... Step 2: Find each score's deviation from the mean. ... Step 3: Square each deviation from the mean. ... Step 4: Find the sum of squares. ... Step 5: Divide the sum of squares by n – 1 or N.

kenneth

what is error

Yakuba Reply

Is mistake done to something

Vutshila

anas

What is the life teble

anas

Jibrin

statistics is the analyzing of data

Tajudeen Reply

what is statics?

Zelalem Reply

how do you calculate mean

Gloria Reply

diveving the sum if all values

Shaynaynay

let A1,A2 and A3 events be independent,show that (A1)^c, (A2)^c and (A3)^c are independent?

Fisaye Reply

what is statistics

Akhisani Reply

data collected all over the world

Shaynaynay

construct a less than and more than table

Imad Reply

The sample of 16 students is taken. The average age in the sample was 22 years with astandard deviation of 6 years. Construct a 95% confidence interval for the age of the population.

Aschalew Reply

Bhartdarshan' is an internet-based travel agency wherein customer can see videos of the cities they plant to visit. The number of hits daily is a normally distributed random variable with a mean of 10,000 and a standard deviation of 2,400 a. what is the probability of getting more than 12,000 hits? b. what is the probability of getting fewer than 9,000 hits?

Akshay Reply

Bhartdarshan'is an internet-based travel agency wherein customer can see videos of the cities they plan to visit. The number of hits daily is a normally distributed random variable with a mean of 10,000 and a standard deviation of 2,400. a. What is the probability of getting more than 12,000 hits

Akshay

Bright

Sorry i want to learn more about this question

Bright

Someone help

Bright

a= 0.20233 b=0.3384

Sufiyan

Shaynaynay

How do I interpret level of significance?

Mohd Reply

It depends on your business problem or in Machine Learning you could use ROC- AUC cruve to decide the threshold value

Shivam

how skewness and kurtosis are used in statistics

Owen Reply

yes what is it

Taneeya

Got questions? Join the online conversation and get instant answers!

Jobilize.com Reply

<< Chapter < Page Page > Chapter >>

Read also:

Get Jobilize Job Search Mobile App in your pocket Now!

100% Free Mobile Applications
Receive real-time job alerts and never miss the right job again

Source: OpenStax, Automatic white blood cell classification using svm and neural networks. OpenStax CNX. Dec 16, 2015 Download for free at http://legacy.cnx.org/content/col11924/1.5

Google Play and the Google Play logo are trademarks of Google Inc.

Notification Switch

Would you like to follow the 'Automatic white blood cell classification using svm and neural networks' conversation and receive update notifications?

Ask

	Precalculus By OpenStax Read Online Course
	NCE Ch 08 Appraisal By Anh Dao Start Quiz
©flickr:	Anatomy Physiology By Jemekia Weeden Start Quiz
©flickr: Luis	Atoms By Carly Allen Start Quiz
	2 Neuroanatomy 02 Brain External Features By Stephen Voron Start Quiz
	32 Biology 32 Plant Reproduction MCQ By OpenStax Start Quiz
	Financial Markets By Keyaira Braxton Start Exam
	Professional Writing MCQ By Mary Cohen Start Quiz
©flickr: U.S.	Molecular Cellular Biology By Ann Schlosser Start Quiz
©flickr:	Math for Economists MCQ By Tony Pizur Start Quiz