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malaria detection code

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moonanjum26
2018-08-22 22:21:36 +05:30
parent efbe825bd9
commit ab3036cec2
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181
src/rashmi's code/CellSegmentation.m Normal file
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clc ;
close all;
clear all;
%mkdir Live;
mkdir slice;
%% Code to write images
%open(NewObj1);
%open(NewObj2);
updating_array=0;
dead_cells=0;
total_dead_cells=0;
total_live_cells=0;
live_cells=0;
for videos=91:91
z=0;
VidObj = VideoReader(strcat('D:\All_videos\Yeast_1\yeast00',num2str(videos),'.avi'));
VidObjDat = read(VidObj);
Nframes = VidObj.NumberOfFrames;
vidHeight = VidObj.Height;
vidWidth = VidObj.Width;
mean_count=1;
pixel_count=1;
%% Generate Background
a = read(VidObj,1);
%%% average ten frames to generate background.
bg = 0;
count = 30 ;% set number of frame to be averaged
N = 1; % start frame number
for i = N:N+count
bg = bg +double(rgb2gray(read(VidObj,i)));
end
bg = uint8(bg /count); % Final Background generated.
%imshow(bg);
% set appropriate cropping for the background
% RS = 135;% start value of the row
%RE = 280; % end value of the row
RS = 76;% start value of the row
RE = 280;% start value of the row
% RS = 493;% start value of the row
% RE = 638; % end value of the row
CS = 1;% start value of the column
CE = 216; % end value of the column
% crop as per the following imshow(CS:CE,RS:RE)
%imshow(bg(:,RS:RE));
%% subtract all subsequent frames and write into a video.
ccount = 0;
gccount = 0;
framecount = 0;
ccount = 0;
MAVector = 0;
MVector = 0;
framenum = 0;
pframenum = 0;
for k =1:Nframes
Copy=rgb2gray(read(VidObj,k));
CurrFrame = double(rgb2gray(read(VidObj,k)));
CurrFrame1=read(VidObj,k);
CurrFrame1=CurrFrame1(:,RS:RE);
Sub = double(CurrFrame(:,RS:RE)-double(bg(:,RS:RE)));
Submin = min(Sub(:));
Submax = max(Sub(:));
AdjBGSub = uint8( (Sub - Submin)/(Submax-Submin) * 255);
I = AdjBGSub;
[A, B]=size(I);
% a = uint8(min(Sub(:))*-1+Sub);
[~, threshold] = edge(I, 'sobel');
fudgeFactor = 1;
BWs = edge(I,'sobel', threshold * fudgeFactor);
se90 = strel('line', 3, 90);
se0 = strel('line', 3, 0);
BWsdil = imdilate(BWs, [se90 se0]);
BWdfill = imfill(BWsdil, 'holes');
BWdfillopen = imopen(BWdfill,strel('disk',5,4));
BWnobord = imclearborder(BWdfillopen, 4);
seD = strel('diamond',2);
BWfinal = imerode(BWnobord,seD);
BWfinal = imerode(BWfinal,seD);
CC =bwconncomp(BWfinal);
Areas = regionprops(CC,'Area');
MALength = regionprops(CC,'MajorAxisLength');
MinLength = regionprops(CC,'MinorAxisLength');
Centroids=regionprops(CC,'Centroid');
BWthresh = imcomplement(im2bw(I,0.15));
% imshow(BWthresh);
% figure;
% imshow(uint8(BWthresh).*I);
% CC_par =bwconncomp(BWthresh);
% Centroids=regionprops(CC_par,'Centroid');
% Areas_par = regionprops(CC_par,'Area');
for il = 1:CC.NumObjects
if(Areas(il).Area > 40 && Areas(il).Area < 45840 )
ccount = ccount +1;
gccount = gccount +1;
CR1=round(Centroids(il).Centroid(1))-15;
CC1=round(Centroids(il).Centroid(2))-15;
CR2=round(Centroids(il).Centroid(1))+15;
CC2=round(Centroids(il).Centroid(2))+15;
if CR1 <= 0
CR1=1;
end
if CC1 <= 0
CC1=1;
end
if CR2 > B
CR2=B;
end
if CC2 > A
CC2=A;
end
sliceOfImage = CurrFrame1(CC1:CC2,CR1:CR2);
%imwrite(sliceOfImage,strcat('C:\Users\RBCCPS\Documents\MATLAB\slice/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
%mean(mean_count)=mean2(I(CC1:CC2,CR1:CR2));
%mean_count=mean_count+1;
%mean_slice=mean2(sliceOfImage);
[p, q]=size(sliceOfImage);
% BWthresh = im2bw(sliceOfImage,0.15);
% imshow(BWthresh);
% figure;
% imshow(uint8(BWthresh).*I);
%CC1 =bwconncomp(imcomplement(BWthresh));
% Areas_stained = regionprops(CC1,'Area');
% C_Percentstained = sum(struct2array(Areas_stained))/max(struct2array(Areas))*100;
% Percentstained = horzcat(Percentstained, C_Percentstained );
z=z+1; %if (pixel_count ~=0)
pixel_count_array(z)=pixel_count;
imwrite(sliceOfImage,strcat('C:\Users\RBCCPS\Documents\MATLAB\slice/FrameNumber','-',num2str(videos),'-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
end
end
end
updating_array=vertcat(updating_array(:),pixel_count_array(:));
pixel_count_array=0;
z=0;
videos
end
%end
%end
%end
%close(NewObj1);
%close(NewObj2);--

302
src/rashmi's code/CellSegmentationRefined.m Normal file
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%%
%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;
initialFrame=1646;
finalFrame=1646;
folder='D:\videos\May10\n3\';
mkdir(strcat(folder,'RBCs'));
mkdir(strcat(folder,'Parasites'));
mkdir(strcat(folder,'Clusters'));
mkdir(strcat(folder,'Mask'));
mkdir(strcat(folder,'choppedCells'));
mkdir(strcat(folder,'Platelets'));
mkdir(strcat(folder,'FalseNegatives'));
mkdir(strcat(folder,'Falsepositive'));
%%
RBCCount=0; parCount=0; clusterCount=0; Fcount=0; choppedCells=0;platelets=0;
Percentstained = 0; C_Percentstained = 0;
count = 0; mainA = 0; StainA = 0;circularity=[];circularity_par=[];circularity_rbc=[];
P_axisRatio=[]; P_majAxis=[]; P_minAxis=[]; P_area=[];
C_axisRatio=[]; C_majAxis=[]; C_minAxis=[]; C_area=[];
stainedpercent = [];
area_par=[];
MajAxis_par=[]; MinAxis_par=[]; AxisRatio_par=[];
%%
% 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.
fcount=0;
% 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 = 330; %center X pixel of the circle
centerY = 260; %center y pixel of the circle
radius = 160; %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;
count = 300 ;% set number of frame to be averaged
N = 2; % start frame number
for i = N:N+count
% bg = bg +double(rgb2gray((imread(strcat('D:\videos\S\S1\1 (',num2str(V+i),').jpg')))));
imagename=char(sprintf('%08d',i));
bg = bg +double(rgb2gray(imread(strcat(folder,imagename,'.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 k =initialFrame:finalFrame
k
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(k,2000) == 0
%if V <19999-30
% bg1 = bg;
bg =0;
count = 300 ;% set number of frame to be averaged
N = k; % start frame number
for i = N:N+count
% bg = bg +double(rgb2gray((imread(strcat('D:\videos\S\S1\1 (',num2str(V+i),').jpg')))));
imagename=char(sprintf('%08d',i));
bg = bg +double(rgb2gray(imread(strcat(folder,imagename,'.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
imagename=char(sprintf('%08d',k));
CurrFrame = (rgb2gray((imread(strcat(folder,imagename,'.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(k) = 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(k)+stdIntensityValue*4; % mean+ 4*standard deviation
Imin = meanIntensityValue(k)-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);%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]);k
CC =bwconncomp(Maskfinal);
AxisRatio=[];
Centroids = regionprops(CC,'Centroid');
Areas = regionprops(CC,'Area');
Perimeters=regionprops(CC,'Perimeter');
%mainA = horzcat(mainA, max(struct2array(Areas)));
for il = 1:CC.NumObjects
if (CC.NumObjects>0)
if (Areas(il).Area > 500 && Areas(il).Area < 5000 )
ccount=ccount+1;
X = Centroids(il).Centroid(1);
Y = Centroids(il).Centroid(2);
lowX = round(X)-20;
lowY = round(Y)-20;
HighX = round(X)+20;
HighY = round(Y)+20;
if lowX <= 0
lowX=1;
end
if lowY <= 0
lowY=1;
end
if HighX > B
HighX=B;
end
if HighY > A
HighY=A;
end
I1=Ia(lowY:HighY,lowX:HighX);
circularity(ccount) = Perimeters(il).Perimeter ^ 2 / (4 * pi * Areas(il).Area);
BWthresh = im2bw(I1,0.2);
%imtool(BWthresh);
% Par_dil = imdilate(BWthresh, [se90 se0]);
% Par_fill = imfill(BWthresh, 'holes');
imwrite(Maskfinal,strcat(folder,'Mask/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
%imtool(BWthresh)
% imshow(BWthresh);
% figure;
% imshow(uint8(BWthresh).*Ia);
CC1 =bwconncomp(imcomplement(BWthresh));
Areas_stained = regionprops(CC1,'Area');
MajAxis= regionprops(CC1,'MajorAxisLength');
MinAxis= regionprops(CC1,'MinorAxisLength');
AxisRatio=struct2array(MajAxis)/struct2array(MinAxis);
struct2array(MajAxis);
struct2array(MinAxis);
Areas_stained.Area;
C_Percentstained = sum(struct2array(Areas_stained))/max(struct2array(Areas))*100;
Percentstained = horzcat(Percentstained, C_Percentstained );
StainA = horzcat(StainA, sum(struct2array(Areas_stained)));
MajAxisMat=struct2array(MajAxis);
numMajaxis=size(MajAxisMat,2);
no_majAxis=isempty(MajAxis);
MajAxis_par=[];MinAxis_par=[];
MajAxis_par=(struct2array(MajAxis));
MinAxis_par=(struct2array(MinAxis));
Area_par=struct2array(Areas_stained);
i=1;
f=numMajaxis;
% For loop to compute ratio of major and minor axis
% to check if the object in the BWthresh is long or
% round shaped
AxisRatio_par=[];
for i=1:f
AxisRatio_par(i)=MajAxis_par(i)/MinAxis_par(i)
end
AxisRatio_par
countgreater2=0;
%Another for loop to check if the object has the
%ratio greater than 2.7, it should be classified as
%RBC and not as a parasite.
for i=1:f
if (AxisRatio_par(i) > 2.7)
countgreater2=countgreater2+1;
end
end
if (Areas(il).Area > 1500 && Areas(il).Area < 5000 )
clusterCount=clusterCount+2;
imwrite(I1,strcat(folder,'Clusters/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
%If there are major Axis
elseif (no_majAxis == 0)
if (C_Percentstained<40 && C_Percentstained>3 && AxisRatio < 3 && max(struct2array(MajAxis)) < 18 && numMajaxis < 4 )
stainedpercent(ccount) = C_Percentstained;
area_par(ccount)=Areas(il).Area;
%%If there are no cells having count greater than
%%2.7 i.e countgreater2==0 then the cell is
%%classified as a parasite
if (countgreater2 == 0)
'Parasite1'
parCount=parCount+1;
imwrite(I1,strcat(folder,'Parasites/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
imwrite(BWthresh,strcat(folder,'Falsepositive/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
% 'default'
% if (max(Area_par) > 20 && max(AxisRatio_par) <4 && max(struct2array(MajAxis)) < 25)
% 'Parasite3'
% parCount=parCount+1;
% imwrite(I1,strcat(folder,'Parasites/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
% imwrite(BWthresh,strcat(folder,'Falsepositive/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
elseif (max(struct2array(MajAxis))< 10 && C_Percentstained > 5 && max(AxisRatio_par) < 3.5)
'Parasite2'
parCount=parCount+1;
imwrite(I1,strcat(folder,'Parasites/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
imwrite(BWthresh,strcat(folder,'Falsepositive/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
else
'RBC1'
RBCCount=RBCCount+1;
circularity_rbc(ccount)=Perimeters(il).Perimeter ^ 2 / (4 * pi * Areas(il).Area);
imwrite(I1,strcat(folder,'RBCs/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
imwrite(BWthresh,strcat(folder,'FalseNegatives/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
end
%RBC
elseif ( max(struct2array(MajAxis)) > 15 || numMajaxis>1 || (C_Percentstained<5 && C_Percentstained>1) || AxisRatio > 2)
if (max(struct2array(MajAxis))< 10 && C_Percentstained > 10 && max(AxisRatio_par) < 2 && max(Area_par) < 200)
'Parasite3'
parCount=parCount+1;
imwrite(I1,strcat(folder,'Parasites/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
imwrite(BWthresh,strcat(folder,'Falsepositive/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
else
'RBC2'
RBCCount=RBCCount+1;
circularity_rbc(ccount)=Perimeters(il).Perimeter ^ 2 / (4 * pi * Areas(il).Area);
imwrite(I1,strcat(folder,'RBCs/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
imwrite(BWthresh,strcat(folder,'FalseNegatives/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
end
end
elseif (~isempty(Areas_stained))
if(Areas_stained.Area >25)
parCount=parCount+1;
imwrite(I1,strcat(folder,'Parasites/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
imwrite(BWthresh,strcat(folder,'Falsepositive/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
end
else
'RBC3'
RBCCount=RBCCount+1;
imwrite(I1,strcat(folder,'RBCs/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
imwrite(BWthresh,strcat(folder,'FalseNegatives/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
end
end
end
end
end
Fcount=clusterCount+RBCCount+parCount+choppedCells;