malaria detection code
This commit is contained in:
moonanjum26
181
src/rashmi's code/CellSegmentation.m
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181
src/rashmi's code/CellSegmentation.m
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clc ;
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close all;
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clear all;
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%mkdir Live;
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mkdir slice;
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%% Code to write images
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%open(NewObj1);
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%open(NewObj2);
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updating_array=0;
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dead_cells=0;
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total_dead_cells=0;
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total_live_cells=0;
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live_cells=0;
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for videos=91:91
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z=0;
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VidObj = VideoReader(strcat('D:\All_videos\Yeast_1\yeast00',num2str(videos),'.avi'));
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VidObjDat = read(VidObj);
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Nframes = VidObj.NumberOfFrames;
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vidHeight = VidObj.Height;
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vidWidth = VidObj.Width;
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mean_count=1;
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pixel_count=1;
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%% Generate Background
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a = read(VidObj,1);
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%%% average ten frames to generate background.
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bg = 0;
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count = 30 ;% set number of frame to be averaged
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N = 1; % start frame number
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for i = N:N+count
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bg = bg +double(rgb2gray(read(VidObj,i)));
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end
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bg = uint8(bg /count); % Final Background generated.
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%imshow(bg);
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% set appropriate cropping for the background
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% RS = 135;% start value of the row
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%RE = 280; % end value of the row
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RS = 76;% start value of the row
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RE = 280;% start value of the row
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% RS = 493;% start value of the row
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% RE = 638; % end value of the row
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CS = 1;% start value of the column
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CE = 216; % end value of the column
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% crop as per the following imshow(CS:CE,RS:RE)
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%imshow(bg(:,RS:RE));
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%% subtract all subsequent frames and write into a video.
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ccount = 0;
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gccount = 0;
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framecount = 0;
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ccount = 0;
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MAVector = 0;
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MVector = 0;
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framenum = 0;
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pframenum = 0;
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for k =1:Nframes
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Copy=rgb2gray(read(VidObj,k));
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CurrFrame = double(rgb2gray(read(VidObj,k)));
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CurrFrame1=read(VidObj,k);
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CurrFrame1=CurrFrame1(:,RS:RE);
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Sub = double(CurrFrame(:,RS:RE)-double(bg(:,RS:RE)));
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Submin = min(Sub(:));
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Submax = max(Sub(:));
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AdjBGSub = uint8( (Sub - Submin)/(Submax-Submin) * 255);
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I = AdjBGSub;
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[A, B]=size(I);
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% a = uint8(min(Sub(:))*-1+Sub);
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[~, threshold] = edge(I, 'sobel');
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fudgeFactor = 1;
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BWs = edge(I,'sobel', threshold * fudgeFactor);
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se90 = strel('line', 3, 90);
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se0 = strel('line', 3, 0);
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BWsdil = imdilate(BWs, [se90 se0]);
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BWdfill = imfill(BWsdil, 'holes');
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BWdfillopen = imopen(BWdfill,strel('disk',5,4));
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BWnobord = imclearborder(BWdfillopen, 4);
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seD = strel('diamond',2);
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BWfinal = imerode(BWnobord,seD);
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BWfinal = imerode(BWfinal,seD);
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CC =bwconncomp(BWfinal);
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Areas = regionprops(CC,'Area');
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MALength = regionprops(CC,'MajorAxisLength');
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MinLength = regionprops(CC,'MinorAxisLength');
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Centroids=regionprops(CC,'Centroid');
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BWthresh = imcomplement(im2bw(I,0.15));
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% imshow(BWthresh);
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% figure;
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% imshow(uint8(BWthresh).*I);
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% CC_par =bwconncomp(BWthresh);
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% Centroids=regionprops(CC_par,'Centroid');
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% Areas_par = regionprops(CC_par,'Area');
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for il = 1:CC.NumObjects
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if(Areas(il).Area > 40 && Areas(il).Area < 45840 )
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ccount = ccount +1;
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gccount = gccount +1;
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CR1=round(Centroids(il).Centroid(1))-15;
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CC1=round(Centroids(il).Centroid(2))-15;
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CR2=round(Centroids(il).Centroid(1))+15;
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CC2=round(Centroids(il).Centroid(2))+15;
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if CR1 <= 0
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CR1=1;
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end
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if CC1 <= 0
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CC1=1;
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end
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if CR2 > B
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CR2=B;
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end
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if CC2 > A
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CC2=A;
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end
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sliceOfImage = CurrFrame1(CC1:CC2,CR1:CR2);
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%imwrite(sliceOfImage,strcat('C:\Users\RBCCPS\Documents\MATLAB\slice/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
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%mean(mean_count)=mean2(I(CC1:CC2,CR1:CR2));
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%mean_count=mean_count+1;
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%mean_slice=mean2(sliceOfImage);
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[p, q]=size(sliceOfImage);
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% BWthresh = im2bw(sliceOfImage,0.15);
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% imshow(BWthresh);
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% figure;
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% imshow(uint8(BWthresh).*I);
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%CC1 =bwconncomp(imcomplement(BWthresh));
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% Areas_stained = regionprops(CC1,'Area');
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% C_Percentstained = sum(struct2array(Areas_stained))/max(struct2array(Areas))*100;
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% Percentstained = horzcat(Percentstained, C_Percentstained );
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z=z+1; %if (pixel_count ~=0)
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pixel_count_array(z)=pixel_count;
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imwrite(sliceOfImage,strcat('C:\Users\RBCCPS\Documents\MATLAB\slice/FrameNumber','-',num2str(videos),'-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
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end
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end
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end
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updating_array=vertcat(updating_array(:),pixel_count_array(:));
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pixel_count_array=0;
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z=0;
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videos
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end
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%end
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%end
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%end
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%close(NewObj1);
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%close(NewObj2);--
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302
src/rashmi's code/CellSegmentationRefined.m
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302
src/rashmi's code/CellSegmentationRefined.m
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@@ -0,0 +1,302 @@
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%%
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%The code creates a feature table for the different types of cells and
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%is used to create a initial training dataset for the classifier
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%%
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clc ;
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close all;
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clear all;
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initialFrame=1646;
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finalFrame=1646;
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folder='D:\videos\May10\n3\';
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mkdir(strcat(folder,'RBCs'));
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mkdir(strcat(folder,'Parasites'));
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mkdir(strcat(folder,'Clusters'));
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mkdir(strcat(folder,'Mask'));
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mkdir(strcat(folder,'choppedCells'));
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mkdir(strcat(folder,'Platelets'));
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mkdir(strcat(folder,'FalseNegatives'));
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mkdir(strcat(folder,'Falsepositive'));
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%%
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RBCCount=0; parCount=0; clusterCount=0; Fcount=0; choppedCells=0;platelets=0;
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Percentstained = 0; C_Percentstained = 0;
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count = 0; mainA = 0; StainA = 0;circularity=[];circularity_par=[];circularity_rbc=[];
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P_axisRatio=[]; P_majAxis=[]; P_minAxis=[]; P_area=[];
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C_axisRatio=[]; C_majAxis=[]; C_minAxis=[]; C_area=[];
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stainedpercent = [];
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area_par=[];
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MajAxis_par=[]; MinAxis_par=[]; AxisRatio_par=[];
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%%
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% To create a circular mask to eliminate the spokes channels and perform
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% processing of cells in the central imaging region
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% Create a logical image of a circle with specified
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% diameter, center, and image size.
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fcount=0;
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% First create the image.
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imageSizeX = 640; %X pixel size of the image
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imageSizeY = 480; %Y pixel size of the image
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[columnsInImage, rowsInImage] = meshgrid(1:imageSizeX, 1:imageSizeY);
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% Next create the circle in the image.
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centerX = 330; %center X pixel of the circle
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centerY = 260; %center y pixel of the circle
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radius = 160; %Radius of the circle
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circlePixels = (rowsInImage - centerY).^2 ...
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+ (columnsInImage - centerX).^2 <= radius.^2; % creates the circular mask
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%% Generate Background
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%a = rgb2gray(imread(strcat('E:\Experiments\odroid\step1\1 (',num2str(V),').jpg')));
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%imshow(a);
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%%% average ten frames to generate background.
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bg = 0;
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count = 300 ;% set number of frame to be averaged
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N = 2; % start frame number
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for i = N:N+count
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% bg = bg +double(rgb2gray((imread(strcat('D:\videos\S\S1\1 (',num2str(V+i),').jpg')))));
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imagename=char(sprintf('%08d',i));
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bg = bg +double(rgb2gray(imread(strcat(folder,imagename,'.jpg'))));
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end
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bg = uint8(bg /count); % Final Background generated.
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imshow(uint8(circlePixels).*bg);
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%%
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tic
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r =0;b=0;
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count = 0;
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maxglcm = 0;
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Cont =0;Corr=0;Homo=0;I_mean=0;I_std=0;I_cir=0;I_area =0;stain=0;
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ccount = 0;
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C_Percentstained =0;StainMaxlength=0;StainMinlength=0;StainSolid=0; StainNumobj=0;
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Features_table = [];F=[];
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CellFeature_table = [];cell=[];
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% Feature_table = table('VariableNames',{'Area', 'ConvexArea', 'Eccentricity', 'EquivDiameter', 'EulerNumber', 'Extent', 'FilledArea', 'MajorAxisLength', 'MinorAxisLength', 'Orientation', 'Perimeter', 'Solidity'});
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for k =initialFrame:finalFrame
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k
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Shape_Features =[]; Texture_Features1 = [];
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% The if condition is used to refresh the background for every 2000 frames
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% This helps eliminate any debris/ struck cells in the ROI
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if mod(k,2000) == 0
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%if V <19999-30
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% bg1 = bg;
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bg =0;
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count = 300 ;% set number of frame to be averaged
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N = k; % start frame number
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for i = N:N+count
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% bg = bg +double(rgb2gray((imread(strcat('D:\videos\S\S1\1 (',num2str(V+i),').jpg')))));
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imagename=char(sprintf('%08d',i));
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bg = bg +double(rgb2gray(imread(strcat(folder,imagename,'.jpg'))));
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end
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bg = uint8(bg /count); % Final Background generated.
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% bg = (bg+bg1)/2.0
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end
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% The following section performs the segmentation based on histogram values
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imagename=char(sprintf('%08d',k));
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CurrFrame = (rgb2gray((imread(strcat(folder,imagename,'.jpg')))));
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Sub =double(double(CurrFrame.*uint8(circlePixels))-double(bg.*uint8(circlePixels)));
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%imshow(Sub);
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Submin = min(Sub(:));
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Submax = max(Sub(:));
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AdjBGSub = uint8( (Sub - Submin)/(Submax-Submin) * 255);
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Ia = AdjBGSub;
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[A, B]=size(Ia);
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I = medfilt2(Ia); % To smoothen the image
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I = adapthisteq(I);
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I1 = medfilt2(I);
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Irgb = cat(3, I1, I1, I1);
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meanIntensityValue(k) = mean2(I1); % Finds the mean of the intensities of the image pixels
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stdIntensityValue = std2(I1); % Finds the standard deviation of the intensities of the image pixels
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% Selection of max and minimum of intensities for the thresholding
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%This multipication factor can be varied when you are optimizing the
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%thresholding
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Imax = meanIntensityValue(k)+stdIntensityValue*4; % mean+ 4*standard deviation
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Imin = meanIntensityValue(k)-stdIntensityValue*4; % mean- 4*standard deviation
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% Further processing of the thresholded image
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Mask = createMask(Irgb,Imax,Imin);%figure;imshow(Mask);
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MaskInv = ~Mask;
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Maskopen =bwareaopen(MaskInv,150);%figure;imshow(Maskfinal);
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% Maskdil = imdilate(Maskopen, [se90 se0]);%figure;imshow(Maskdil);
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Maskdil = imclose(Maskopen, strel('disk',5));
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Maskfill = imfill(Maskdil, 'holes');%figure;imshow(Maskfill);
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Maskclose = Maskfill;
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% Maskclose = imclose(Maskfill, strel('disk',5));
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Maskclear = imclearborder(Maskclose);%figure;imshow(Maskclear);
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Maskfinal = Maskclear;
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% % Filter image based on image properties.
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% Maskfinal = bwpropfilt(Maskfinal, 'Area', [350 + eps(350), Inf]); % Area greater than 350 pixels
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% Maskfinal = bwpropfilt(Maskfinal, 'Solidity', [0.6 + eps(0.6), Inf]); % 1 is completely solid region
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% Maskfinal = bwpropfilt(Maskfinal, 'EulerNumber', [4.94065646e-324 + eps(4.94065646e-324), Inf]);k
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CC =bwconncomp(Maskfinal);
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AxisRatio=[];
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Centroids = regionprops(CC,'Centroid');
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Areas = regionprops(CC,'Area');
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Perimeters=regionprops(CC,'Perimeter');
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%mainA = horzcat(mainA, max(struct2array(Areas)));
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for il = 1:CC.NumObjects
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if (CC.NumObjects>0)
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if (Areas(il).Area > 500 && Areas(il).Area < 5000 )
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ccount=ccount+1;
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X = Centroids(il).Centroid(1);
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Y = Centroids(il).Centroid(2);
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lowX = round(X)-20;
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lowY = round(Y)-20;
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HighX = round(X)+20;
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HighY = round(Y)+20;
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if lowX <= 0
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lowX=1;
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end
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if lowY <= 0
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lowY=1;
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end
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if HighX > B
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HighX=B;
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end
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if HighY > A
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HighY=A;
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end
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I1=Ia(lowY:HighY,lowX:HighX);
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circularity(ccount) = Perimeters(il).Perimeter ^ 2 / (4 * pi * Areas(il).Area);
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BWthresh = im2bw(I1,0.2);
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%imtool(BWthresh);
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% Par_dil = imdilate(BWthresh, [se90 se0]);
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% Par_fill = imfill(BWthresh, 'holes');
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imwrite(Maskfinal,strcat(folder,'Mask/FrameNumber','-',num2str(k),'-',num2str(il),'.jpg'),'jpeg');
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%imtool(BWthresh)
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% imshow(BWthresh);
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% figure;
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% 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;
|
||||||
|
|
||||||
|
|
||||||
Reference in New Issue
Block a user