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Leukocyte Images Classification

System for the automated classification of white blood cells (leukocytes). Input is a WBC image and the output is the class of the given image.

You can download the LISC (Leukocyte Images for Segmentation and Classification) database from the following link: http://users.cecs.anu.edu.au/~hrezatofighi/Data/Leukocyte%20Data.htm

An example of each of the five classes of white blood cell is shown in the following figure:

Phase 1: Segmentation

Load a random specimen as HSV

Different color spaces has been tested (RGB, LAB, LIN) and found HSV is most suitable for the given task.

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img = rgb2hsv(imread('specimen.bmp'));
% Extract the second layer
layer = img(:, :, 2);
imshow(layer);
Original RGB Sample HSV's saturation layer

Isolate nucleus

The process mainly involved carefully incrementing the threshold until an optimal segment has been found.

Here is the result of a careful iterative process over a large sample to extract the nucleus.

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nucleusThreshold = 0.3686;
p1 = layer > nucleusThreshold;
imshow(p1);

Prune nucleus

Small unwanted artifacts can be removed by an open mask with a disk structural element of size 8.

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se = strel('disk', 8);
p1 = imopen(p1, se);

Isolate surrounding fluid

The surrounding fluid is essential in identifying variations of whiteblood cells.

Similar process of isolating the nucleus has been used to isolate the fluid.

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fluidThreshold = 0.0980;
p2 = layer > fluidThreshold;
imshow(p2);

Prune fluid

Same as nucleus, unwanted artifacts can be removed by an open mask with a disk structural element but with a size 28.

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se = strel('disk', 28);
p2 = imopen(p2, se);
imshow(p2);

Merge nucleus with fluid

Here the fluid is given a more distinguished color (used 150 grey) then added to the nucleus.

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p1 = im2uint8(p1);
p2 = im2uint8(p2);
p2(p2 == 255) = 150; % distinguish fluid from nucleus
imshow(p1 + p2); % combine components

Examples

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SAMPLE_SIZE = 6;
for i=1:SAMPLE_SIZE
res = imread('samples/' + string(i) + '_res.png');
ref = imread('samples/' + string(i) + '_expert.bmp');
% figure, imshowpair(res, ref, 'montage');
subplot(1, 2, 1), imshow(res);
subplot(1, 2, 2), imshow(ref);
figure;
end
Segmenter Ground Truth

Phase 2: Feature Extraction

Deep Learning is a machine learning technique that can learn useful representations and features directly from images. A CNN learn these features automatically from images from generic low level features like edge and corners all the way to specific problem features.

Deep Learning algorithms not only perform classification but also learn to extract features directly from image, thereby eliminate the need for manual feature extraction.

Instead of training a CNN from scratch, we use a pre-trained CNN model (VGG-19) as an automatic feature extract known as Transfer Learning.

VGG-19 has been trained on over a million images and can classify images into 1000 object categories (such as keyboard, coffee mug, pencil, and many animals).

The network has learned rich feature representations for a wide range of images. The network takes an image as input and outputs a label for the object in the image together with the probabilities for each of the object categories.

Here a demonstration on how to fine-tune a pre-trained VGG-19 CNN to perform classification on LISC image database.

Training Model

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%% Load Dataset
dsTrain = imageDatastore('train', 'IncludeSubfolders', true, ...
'LabelSource', 'foldernames');
dsValidate = imageDatastore('validate', 'IncludeSubfolders', true, ...
'LabelSource', 'foldernames');
%% Resize Dataset
augTrain = augmentedImageDatastore([224 224], dsTrain);
augValidate = augmentedImageDatastore([224 224], dsValidate);
%% Load VGG-19 network
net = vgg19();
analyzeNetwork(net)
%% Replace last layers
layersTransfer = net.Layers(1:end-3);
numClasses = numel(categories(dsTrain.Labels))

layers = [
layersTransfer
fullyConnectedLayer(numClasses,'WeightLearnRateFactor', 20, ...
'BiasLearnRateFactor', 20)
softmaxLayer
classificationLayer];

%% Network options
options = trainingOptions('sgdm', ...
'MiniBatchSize',10, ...
'MaxEpochs',6, ...
'InitialLearnRate',1e-4, ...
'Shuffle','every-epoch', ...
'ValidationData',augValidate, ...
'ValidationFrequency',3, ...
'Verbose',false, ...
'Plots','training-progress');
%% Train Network
vgg19LISC = trainNetwork(augTrain, layers, options);

Layers Details

Layer Number Name Type Description
1 input Image Input 224x224x3 images with 'zerocenter' normalization
2 conv1_1 Convolution 64 3x3x3 convolutions with stride [1 1] and padding [1 1 1 1]
3 relu1_1 ReLU ReLU
4 conv1_2 Convolution 64 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1]
5 relu1_2 ReLU ReLU
6 pool1 Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
7 conv2_1 Convolution 128 3x3x64 convolutions with stride [1 1] and padding [1 1 1 1]
8 relu2_1 ReLU ReLU
9 conv2_2 Convolution 128 3x3x128 convolutions with stride [1 1] and padding [1 1 1 1]
10 relu2_2 ReLU ReLU
11 pool2 Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
12 conv3_1 Convolution 256 3x3x128 convolutions with stride [1 1] and padding [1 1 1 1]
13 relu3_1 ReLU ReLU
14 conv3_2 Convolution 256 3x3x256 convolutions with stride [1 1] and padding [1 1 1 1]
15 relu3_2 ReLU ReLU
16 conv3_3 Convolution 256 3x3x256 convolutions with stride [1 1] and padding [1 1 1 1]
17 relu3_3 ReLU ReLU
18 conv3_4 Convolution 256 3x3x256 convolutions with stride [1 1] and padding [1 1 1 1]
19 relu3_4 ReLU ReLU
20 pool3 Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
21 conv4_1 Convolution 512 3x3x256 convolutions with stride [1 1] and padding [1 1 1 1]
22 relu4_1 ReLU ReLU
23 conv4_2 Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
24 relu4_2 ReLU ReLU
25 conv4_3 Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
26 relu4_3 ReLU ReLU
27 conv4_4 Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
28 relu4_4 ReLU ReLU
29 pool4 Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
30 conv5_1 Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
31 relu5_1 ReLU ReLU
32 conv5_2 Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
33 relu5_2 ReLU ReLU
34 conv5_3 Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
35 relu5_3 ReLU ReLU
36 conv5_4 Convolution 512 3x3x512 convolutions with stride [1 1] and padding [1 1 1 1]
37 relu5_4 ReLU ReLU
38 pool5 Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
39 fc6 Fully Connected 4096 fully connected layer
40 relu6 ReLU ReLU
41 drop6 Dropout 50% dropout
42 fc7 Fully Connected 4096 fully connected layer
43 relu7 ReLU ReLU
44 drop7 Dropout 50% dropout
45 fc Fully Connected 5 fully connected layer
46 softmax Softmax softmax
47 classoutput Classification Output crossentropyex with 'baso' and 4 other classes

Training Result

Using Model

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%% Load Existing Model
load('vgg19LISC.mat');

sample = imread('sample');
sample = imresize(sample, [224 224]); %% Dont forget to resize sample

Repository

Code, training and validating datasets used are available here.