MATLAB: DICOM Image Resize Error

alexnetcreating images for alexnetdeep learningdicomresize

Hi,there. I'm new to matlab and programming.

Since resizing is needed for Alexnet and Googlenet, I`m now working on resizing Dicom File from 256*256*1 to 227*227*3.

Here is my directory:

main

— a

— image.dcm(10 dicom file)

　– b

　　– image.dcm(10 dicom file)

　– c

　　– image.dcm(10 dicom file)

— d

　　– image.dcm(10 dicom file).

Here is my code:

%path = current directory
currentdirectory = pwd;
%% Create an ImageDatastore to help you manage the data.currentdirectory = pwd;
categories = {'a', 'b', 'c','d'}; 
%Because ImageDatastore operates on image file locations, 
imds = imageDatastore(fullfile(currentdirectory, categories),'IncludeSubfolders',true,'FileExtensions','.dcm','LabelSource', 'foldernames','ReadFcn',@dicomread);
%resize
imdsResized = imresize3(imds, [227 227 3]);

However, it throws following error:

Error using imresize3

Expected input number 1, V, to be one of these types:

single, double, int8, int16, int32, uint8, uint16, uint32

Instead its type was matlab.io.datastore.ImageDatastore.

Anyone have answer about this situation, pls let me know.

Thanks in advance!

Best Answer

It is not possible to resize an entire imageDatastore. You would need to loop doing readimage() and resize them one by one, or else use readall() to get back a cell array that you could cellfun() a resize operation on.

Related Solutions

MATLAB: How to fix this issue through running the program

you cannot use pre-trained network unless you adjust it to your data

1. for alexnet, this pre-trained network takes 227x227x3 because it deals with RGB images

2. and that also applies to the first ConveNet which takes 3 channels because its kernels have 3 channels, in which you also have to update

3. you must update the last three classification layers to classify based on your classes

i also think that you are trying to resize your lung dataset to 227x227 in which you may lose some of its quality

this code should work for you, and if it's not clear i can clarify it for you

clear all; close all; clc;
imds = imageDatastore('lung dataset-Labeled', ...
    'IncludeSubfolders',true, 'LabelSource','foldernames', ... % this for labeling by folder names
    'FileExtensions','.dcm','ReadFcn',@readDicomDatastoreImage); % this a function
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7);
net = alexnet(); % analyzeNetwork(lgraph)
numClasses = numel(categories(imdsTrain.Labels)); % number of classes = number of folders
imageSize = [227 227]; % you can use here the original dataset size
global GinputSize
GinputSize = imageSize;
lgraph = layerGraph(net.Layers);
lgraph = removeLayers(lgraph, 'fc8');
lgraph = removeLayers(lgraph, 'prob');
lgraph = removeLayers(lgraph, 'output');
% create and add layers
inputLayer = imageInputLayer([imageSize 1], 'Name', net.Layers(1).Name,...
    'DataAugmentation', net.Layers(1).DataAugmentation, ...
    'Normalization', net.Layers(1).Normalization);
lgraph = replaceLayer(lgraph,net.Layers(1).Name,inputLayer);
newConv1_Weights = net.Layers(2).Weights;
newConv1_Weights = mean(newConv1_Weights(:,:,1:3,:), 3); % taking the mean of kernal channels
newConv1 = convolution2dLayer(net.Layers(2).FilterSize(1), net.Layers(2).NumFilters,...
    'Name', net.Layers(2).Name,...
    'NumChannels', inputLayer.InputSize(3),...
    'Stride', net.Layers(2).Stride,...
    'DilationFactor', net.Layers(2).DilationFactor,...
    'Padding', net.Layers(2).PaddingSize,...
    'Weights', newConv1_Weights,...BiasLearnRateFactor 
    'Bias', net.Layers(2).Bias,...
    'BiasLearnRateFactor', net.Layers(2).BiasLearnRateFactor);
lgraph = replaceLayer(lgraph,net.Layers(2).Name,newConv1);
lgraph = addLayers(lgraph, fullyConnectedLayer(numClasses,'Name', 'fc2'));
lgraph = addLayers(lgraph, softmaxLayer('Name', 'softmax'));
lgraph = addLayers(lgraph, classificationLayer('Name','output'));
lgraph = connectLayers(lgraph, 'drop7', 'fc2');
lgraph = connectLayers(lgraph, 'fc2', 'softmax');
lgraph = connectLayers(lgraph, 'softmax', 'output');
% -------------------------------------------------------------------------
augmenter = imageDataAugmenter( ...
    'RandRotation',[-20,20], ...
    'RandXReflection',1,...
    'RandYReflection',1,...
    'RandXTranslation',[-3 3], ...
    'RandYTranslation',[-3 3]);
%augimdsTrain = augmentedImageDatastore([224 224],imdsTrain,'DataAugmentation',augmenter);
%augimdsValidation = augmentedImageDatastore([224 224],imdsValidation,'DataAugmentation',augmenter);
augimdsTrain = augmentedImageDatastore(imageSize,imdsTrain);
augimdsValidation = augmentedImageDatastore(imageSize,imdsValidation);
options = trainingOptions('rmsprop', ...
    'MiniBatchSize',10, ...
    'MaxEpochs',20, ...
    'InitialLearnRate',1e-3, ...
    'Shuffle','every-epoch', ...
    'ValidationData',augimdsValidation, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
net = trainNetwork(augimdsTrain,lgraph,options)
[YPred, probs] = classify(net,augimdsValidation);
accuracy = mean(YPred ==imdsValidation.Labels)
figure
cm=confusionchart (imdsValidation.Labels, YPred);

you also need to update the readDicomDatastoreImage function to resize every image you read with specified size:

function I = readDicomDatastoreImage(filename)
    onState = warning('off', 'backtrace');
    c = onCleanup(@() warning(onState));
    I = dicomread(filename);
    global GinputSize;
    I = imresize(I,GinputSize(1:2));

MATLAB: How to pass images to a cnn for analysis

Hey Teshan,

After you train a neural network for deep learning using trainNetwork into net, follow this procedure:

Use the function augmentedImageDatastore for effective preprocessing of the images
Use the activations function to compute the DL Network layer activations i.e. the image features using the CNN net
Use the predict function to finally make a prediction using the classifier

The overall workflow that I mentioned is available in this example as well.

Hope this helps!

Best Answer

Related Solutions

MATLAB: How to fix this issue through running the program

MATLAB: How to pass images to a cnn for analysis

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