diff --git a/src/albina nirupa/classificationcode.m b/src/albina nirupa/classificationcode.m
new file mode 100644
index 0000000..cf87765
--- /dev/null
+++ b/src/albina nirupa/classificationcode.m	
@@ -0,0 +1,27 @@
+%%
+mkdir('E:\Experiments\odroid\A2\A2a\cells\rbc');
+mkdir('E:\Experiments\odroid\A2\A2a\cells\cluster');
+mkdir('E:\Experiments\odroid\A2\A2a\cells\wbc');
+mkdir('E:\Experiments\odroid\A2\A2a\cells\parasite');
+%%
+%Classifier Function obtained after training the classifier with the
+%generated training data set and the feature table
+% There are 4 different catergories of classification
+X = table2array(varfun(@double, CellFeature_table_A(:,trainedClassifiercells.PredictorNames)));
+cellclass = predict(trainedClassifiercells, X);
+
+for i = 1:length(cellclass)
+    image = imread(strcat('E:\Experiments\odroid\A2\A2a\cells\A1 (',num2str(i),').jpg'));
+    if(cellclass(i)== 'rbc')
+        imwrite(image,strcat('E:\Experiments\odroid\A2\A2a\cells\rbc\',num2str(i),'.jpg'),'jpeg');
+    else if (cellclass(i)== 'wbc')
+        imwrite(image,strcat('E:\Experiments\odroid\A2\A2a\cells\wbc\',num2str(i),'.jpg'),'jpeg');
+    else if (cellclass(i)== 'cluster')
+        imwrite(image,strcat('E:\Experiments\odroid\A2\A2a\cells\cluster\',num2str(i),'.jpg'),'jpeg');
+    else if (cellclass(i)== 'parasite')
+        imwrite(image,strcat('E:\Experiments\odroid\A2\A2a\cells\parasite\',num2str(i),'.jpg'),'jpeg');
+        end
+        end
+        end
+    end
+end
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diff --git a/src/albina nirupa/createMask.m b/src/albina nirupa/createMask.m
new file mode 100644
index 0000000..c6116f4
--- /dev/null
+++ b/src/albina nirupa/createMask.m	
@@ -0,0 +1,37 @@
+function [BW,maskedRGBImage] = createMask(RGB,Imax,Imin)
+%createMask  Threshold RGB image using auto-generated code from colorThresholder app.
+%  [BW,MASKEDRGBIMAGE] = createMask(RGB) thresholds image RGB using
+%  auto-generated code from the colorThresholder App. The colorspace and
+%  minimum/maximum values for each channel of the colorspace were set in the
+%  App and result in a binary mask BW and a composite image maskedRGBImage,
+%  which shows the original RGB image values under the mask BW.
+
+% Auto-generated by colorThresholder app on 12-Dec-2015
+%------------------------------------------------------
+
+
+% Convert RGB image to chosen color space
+I = RGB;
+
+% Define thresholds for channel 1 based on histogram settings
+channel1Min = Imin;%132.000;
+channel1Max = Imax;%182.000;
+
+% Define thresholds for channel 2 based on histogram settings
+channel2Min = channel1Min;%131.000;
+channel2Max = channel1Max;%177.000;
+
+% Define thresholds for channel 3 based on histogram settings
+channel3Min = channel1Min;%130.000;
+channel3Max = channel1Max;%180.000;
+
+% Create mask based on chosen histogram thresholds
+BW = (I(:,:,1) >= channel1Min ) & (I(:,:,1) <= channel1Max) & ...
+    (I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
+    (I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);
+
+% Initialize output masked image based on input image.
+maskedRGBImage = RGB;
+
+% Set background pixels where BW is false to zero.
+maskedRGBImage(repmat(~BW,[1 1 3])) = 0;
diff --git a/src/albina nirupa/whole_blood_segment_odroid.m b/src/albina nirupa/whole_blood_segment_odroid.m
new file mode 100644
index 0000000..3e7a396
--- /dev/null
+++ b/src/albina nirupa/whole_blood_segment_odroid.m	
@@ -0,0 +1,223 @@
+%%
+%The code creates a feature table for the different types of cells and
+%is used to create a initial training dataset for the classifier
+
+%%
+clc ;
+close all;
+clear all;
+%%
+mkdir('E:\Experiments\odroid\S\S1\gate1');
+mkdir('E:\Experiments\odroid\S\S1\gate2');
+mkdir('E:\Experiments\odroid\S\S1\gate3');
+mkdir('E:\Experiments\odroid\S\S1\gate4');
+mkdir('E:\Experiments\odroid\S\S1\P');
+%%
+% To create a circular mask to eliminate the spokes channels and perform 
+% processing of cells in the central imaging region
+% Create a logical image of a circle with specified
+% diameter, center, and image size.
+% First create the image.
+imageSizeX = 640; %X pixel size of the image
+imageSizeY = 480; %Y pixel size of the image
+[columnsInImage rowsInImage] = meshgrid(1:imageSizeX, 1:imageSizeY);
+% Next create the circle in the image.
+centerX = 300; %center X pixel of the circle
+centerY = 240; %center y pixel of the circle
+radius = 220;  %Radius of the circle
+circlePixels = (rowsInImage - centerY).^2 ...
+    + (columnsInImage - centerX).^2 <= radius.^2; % creates the circular mask
+
+%% Generate Background
+%a = rgb2gray(imread(strcat('E:\Experiments\odroid\step1\1 (',num2str(V),').jpg')));
+%imshow(a);
+%%% average ten frames to generate background.
+bg = 0; V=1;
+count = 500 ;% set number of frame to be averaged
+N = 1; % start frame number
+for i = N:N+count
+    bg = bg +double(rgb2gray((imread(strcat('E:\Experiments\odroid\S\S1\1 (',num2str(V+i),').jpg')))));
+end
+bg  = uint8(bg /count); % Final Background generated.
+imshow(uint8(circlePixels).*bg);
+%%
+tic
+r =0;b=0;
+count = 0;
+maxglcm = 0;
+Cont =0;Corr=0;Homo=0;I_mean=0;I_std=0;I_cir=0;I_area =0;stain=0;
+ ccount = 0;
+ C_Percentstained =0;StainMaxlength=0;StainMinlength=0;StainSolid=0; StainNumobj=0;
+ Features_table = [];F=[];
+CellFeature_table = [];cell=[];
+%  Feature_table =  table('VariableNames',{'Area', 'ConvexArea', 'Eccentricity', 'EquivDiameter', 'EulerNumber', 'Extent', 'FilledArea', 'MajorAxisLength', 'MinorAxisLength', 'Orientation', 'Perimeter', 'Solidity'});
+ for V =1:29999
+      disp(V);
+Shape_Features =[]; Texture_Features1 = [];
+% The if condition is used to refresh the background for every 2000 frames
+% This helps eliminate any debris/ struck cells in the ROI
+if mod(V,2000) == 0 
+     %if V <19999-30
+% bg1 = bg;
+bg =0;
+count = 300 ;% set number of frame to be averaged
+N = 1; % start frame number
+for i = N:N+count
+    bg = bg +double(rgb2gray((imread(strcat('E:\Experiments\odroid\S\S1\1 (',num2str(V+i),').jpg')))));
+end
+bg  = uint8(bg /count); % Final Background generated.
+% bg = (bg+bg1)/2.0
+end    
+ % The following section performs the segmentation based on histogram values    
+ CurrFrame = (rgb2gray((imread(strcat('E:\Experiments\odroid\S\S1\1 (',num2str(V),').jpg')))));
+ Sub =double(double(CurrFrame.*uint8(circlePixels))-double(bg.*uint8(circlePixels)));
+ %imshow(Sub);
+ Submin = min(Sub(:));
+ Submax = max(Sub(:));
+ AdjBGSub = uint8( (Sub - Submin)/(Submax-Submin) * 255);
+ Ia = AdjBGSub;
+  [A, B]=size(Ia);
+  I = medfilt2(Ia); % To smoothen the image
+  I = adapthisteq(I);
+I1 = medfilt2(I);
+Irgb = cat(3, I1, I1, I1);
+meanIntensityValue(V) = mean2(I1); % Finds the mean of the intensities of the image pixels
+stdIntensityValue = std2(I1); % Finds the standard deviation of the intensities of the image pixels
+% Selection of max and minimum of intensities for the thresholding
+%This multipication factor can be varied when you are optimizing the
+%thresholding 
+Imax = meanIntensityValue(V)+stdIntensityValue*4; % mean+ 4*standard deviation
+Imin = meanIntensityValue(V)-stdIntensityValue*4; % mean- 4*standard deviation
+% Further processing of the thresholded image
+Mask = createMask(Irgb,Imax,Imin);%figure;imshow(Mask);
+MaskInv = ~Mask;
+Maskopen =bwareaopen(MaskInv,150);%figure;imshow(Maskfinal);
+% Maskdil = imdilate(Maskopen, [se90 se0]);%figure;imshow(Maskdil);
+Maskdil = imclose(Maskopen, strel('disk',5));
+Maskfill = imfill(Maskdil, 'holes');%figure;imshow(Maskfill);
+Maskclose = Maskfill;
+% Maskclose = imclose(Maskfill, strel('disk',5));
+Maskclear = imclearborder(Maskclose, 4);%figure;imshow(Maskclear);
+Maskfinal = Maskclear;
+% Filter image based on image properties.
+Maskfinal = bwpropfilt(Maskfinal, 'Area', [350 + eps(350), Inf]); % Area greater than 350 pixels
+Maskfinal = bwpropfilt(Maskfinal, 'Solidity', [0.6 + eps(0.6), Inf]); % 1 is completely solid region
+Maskfinal = bwpropfilt(Maskfinal, 'EulerNumber', [4.94065646e-324 + eps(4.94065646e-324), Inf]);
+%Extract properties of all the cells in the thresholded image
+Maskproperties = regionprops(Maskfinal, {'Area', 'ConvexArea', 'Eccentricity', 'EquivDiameter', 'EulerNumber', 'Extent', 'FilledArea', 'MajorAxisLength', 'MinorAxisLength', 'Orientation', 'Perimeter', 'Solidity'});
+% Creates a feature table of all the above listed properties for all the
+% segmented cells/regions in the image
+Shape_Features = struct2table(Maskproperties); 
+%Plotting of 200 images to see the performance of segmetation operations
+if V<200
+h= figure;
+subplot(2,3,1);
+subimage(Mask);
+title('Mask');
+
+subplot(2,3,2);
+subimage(Maskfill);
+title('Maskfill');
+
+subplot(2,3,3);
+subimage(Maskdil);
+title('Maskdil');
+
+subplot(2,3,4);
+subimage(uint8(MaskInv).*I1);
+title('Maskclose');
+% subplot(1,2,1);
+subplot(2,3,5);
+subimage(I1);
+title('I1');
+% subplot(1,2,2);
+subplot(2,3,6);
+subimage(uint8(Maskfinal).*I1);
+title(' Maskfinal');
+%saveas(h,strcat('E:\Experiments\odroid\S\S1\gate4\FrameNumber','-',num2str(V)),'jpg');
+  close(h);
+% figure;imshow(uint8(Maskfinal).*I);
+% figure;imshow(I);
+end
+% Generation of the traning images for the classification program
+CComp = bwconncomp(Maskfinal);
+Areas = regionprops(CComp,'Area');
+Centroids1=regionprops(CComp,'Centroid');
+Perimeters=regionprops(CComp,'Perimeter');
+Texture_Features=[];
+for i = 1:CComp.NumObjects
+    Cent = Centroids1(i).Centroid;
+     HighX = round(Cent(1))+20;
+      if HighX>B
+         HighX =B;
+     end
+     HighY = round(Cent(2))+20;
+     if HighY>A
+         HighY =A;
+     end
+     LowX = round(Cent(1))-19;
+     if LowX<=0
+         LowX =1;
+     end
+    
+     LowY = round(Cent(2))-19;
+      if LowY<=0
+         LowY =1;
+      end
+    
+    ccount = ccount +1;%,num2str(V),'.avi'
+    Icrop=Ia(LowY:HighY,LowX:HighX);
+    Icrop1=I(LowY:HighY,LowX:HighX);
+    Maskcrop=Maskfinal(LowY:HighY,LowX:HighX);
+    imwrite(Icrop,strcat('E:\Experiments\odroid\S\S1\gate3\FrameNumber','-',num2str(V),'_',num2str(ccount),'.jpg'));
+    % Additional texture features for the feature table
+    glcm = graycomatrix(Icrop1);%gray level covariance matrix
+     maxglcm(ccount)=max(max(glcm)); %Feature 1
+     stats(ccount) = graycoprops(glcm,{'Contrast','Correlation','homogeneity'});
+     Texture_Features = struct2table(stats(ccount));
+%      Cont(ccount)= stats(ccount).Contrast; %Feature 2
+%      Corr(ccount) = stats(ccount).Correlation; %Feature 3
+%      Homo(ccount)= stats(ccount).Homogeneity;%Feature 4
+%      Ent(ccount)= stats(ccount).Entropy;
+     I_mean(ccount) = mean2(Icrop1); %Feature 5
+     I_std(ccount)=std2(Icrop1); %Feature 6
+     I_cir(ccount) = ((Shape_Features.Perimeter(i))^ 2)/ (4 * pi * Shape_Features.Area(i)); %Feature 7
+     
+     % Percentage of stained area
+      BWthresh = im2bw(Icrop,0.15);
+      BWthreshtemp =~(BWthresh).*Maskcrop;
+      CC1 =bwconncomp(imcomplement(BWthresh).*Maskcrop);
+      MaxLength = regionprops(CC1,'MajorAxisLength');
+      MinLength = regionprops(CC1,'MinorAxisLength');
+      Solid = regionprops(CC1,'Solidity');
+%       imwrite(~BWthresh,strcat('E:\Experiments\odroid\A2\A2a\gate2\FrameNumber','-',num2str(V),'_',num2str(ccount),'.jpg'));
+      Areas_stained = regionprops(CC1,'Area'); 
+      StainNumobj(ccount) = CC1.NumObjects;
+      if CC1.NumObjects ==0
+          C_Percentstained(ccount) = 0;
+          StainMaxlength(ccount) = 0;StainMinlength(ccount) = 0;
+          StainSolid(ccount) = 0; StainSolid(ccount) = 0;
+      else
+          if (max(struct2array(MaxLength))/max(struct2array(MinLength)))<2
+              F = [F; max(struct2array(MaxLength))/max(struct2array(MinLength))];
+%           imwrite(Icrop,strcat('E:\Experiments\odroid\A2\A2a\P\FrameNumber','-',num2str(V),'_',num2str(ccount),'.jpg'));
+          end
+          C_Percentstained(ccount) = sum(struct2array(Areas_stained))/max((Shape_Features.Area(i)))*100;
+          StainMaxlength(ccount) = max(struct2array(MaxLength));
+          StainMinlength(ccount) = max(struct2array(MinLength));
+          StainSolid(ccount) = max(struct2array(Solid));
+          %           Stainproperties = regionprops(BWthreshtemp, {'Area', 'ConvexArea', 'Eccentricity', 'EquivDiameter', 'EulerNumber', 'Extent', 'FilledArea', 'MajorAxisLength', 'MinorAxisLength', 'Orientation', 'Perimeter', 'Solidity'});
+%           Parasite_Features = struct2table(Stainproperties);
+      end
+  Texture_Features1 = [Texture_Features; Texture_Features1]; 
+end
+if CComp.NumObjects>0
+ Features = [Shape_Features Texture_Features1]; %Variable Addition
+ 
+Features_table = [Features_table; Features]; % Table update
+ end
+ cell = table(I_mean', I_std', I_cir', maxglcm', C_Percentstained',StainMaxlength',StainMinlength',StainSolid', StainNumobj');
+ cell.Properties.VariableNames = {'Mean' 'Std' 'Circularity' 'MaxGLCM' 'Percentage_Stain' 'StainMaxlength' 'StainMinlength' 'StainSolid' 'StainNumobj'};
+ CellFeature_table = [Features_table cell]; % Table update
+ end
+ toc
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