I have boundaries of objects in my code. I want to use those boundaries to calculate the features of those objects in my preceding loop. please help fix

Question

Chanille 2023-4-6

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1942819-i-have-boundaries-of-objects-in-my-code-i-want-to-use-those-boundaries-to-calculate-the-features-of

评论： Chanille 2023-4-17

 edges = edge(imgContrast,'Canny');
    se = strel('disk', 1);
    edgesClean = imclose(edges, se);
    edgesClean = imfill(edgesClean, 'holes');
    B = bwboundaries(edgesClean);

[X, Y] = meshgrid(1:columns, 1:rows);
areaOfEachZone = zeros(1, numZones);
profileCounts = nan(1, numZones);
totalArea = nan(1, numZones);
avgSize = nan(1, numZones);
zoneArea = nan(1, numZones);
avgCircularity = nan(1, numZones);
avgFeret = nan(1, numZones);
avgMinFeret = nan(1, numZones);
for k = 1:numZones
    % Create a binary mask for this zone
    zoneMask = (sqrt((X - x).^2 + (Y - y).^2) >= radius(k)) & (sqrt((X - x).^2 + (Y - y).^2) < radius(k+1));
    areaOfEachZone(k) = sum(zoneMask, 'all') * pixelSize^2;
 
    % Calculate connected components in this zone
    cc = bwconncomp(zoneMask);
    
    % Calculate region properties for each connected component in this zone
    statscc = regionprops(cc, 'Area', 'Centroid', 'Eccentricity', 'Perimeter', 'MajorAxisLength', 'MinorAxisLength','Circularity');
    
    % Calculate region properties for each object and store in arrays
%     areas = [];
    areaOfEachZone = [];
    feretDiams = [];
    circularities = NaN(size(B,1),1); % initialize circularities array to NaN
    
    for j = 1:size(B,1)
        
        boundary = B{j};
        objectMask = poly2mask(boundary(:,2), boundary(:,1), size(AI,1), size(AI,2));
        regionProps = regionprops(objectMask, 'Area', 'Perimeter', 'MaxFeretProperties');
    
        % Calculate circularity
        if ~isempty(regionProps) && size(regionProps, 1) == 1 && regionProps.Area > 0 % exclude regions with zero area
            circularity = 4*pi*regionProps.Area/regionProps.Perimeter^2;
            if circularity >= 0 && circularity <= 1 % circularity should be between 0 and 1
                circularities(j) = circularity;
            else
                circularities(j) = NaN;
            end
            areaOfEachZone(j) = regionProps.Area;
            feretDiams(j) = regionProps.MaxFeretDiameter;
        else
            areaOfEachZone(j) = 0;
            circularities(j) = NaN;
            feretDiams(j) = 0;
        end
    end
    % Calculate features for this zone
    numObjects = cc.NumObjects;
    if numObjects > 0
        areas = [statscc.Area];
        profileCounts(k) = numObjects;
        totalArea(k) = sum(areas) * pixelSize^2;
%         zoneArea(k) = areaOfEachZone(k);
        % Calculate average size of mitochondria for this zone
        avgSize(k) = mean(areas) * pixelSize^2;
%         circularities = [statscc.Circularity];
        avgCircularity(k) = mean(circularities(isfinite(circularities)));
%         ferets = [statscc.MajorAxisLength];
        feretDiams(j) = regionProps.MaxFeretDiameter;
        avgFeret(k) = mean(feretsDiams) * pixelSize;
        minFerets = [statscc.MinorAxisLength];
        avgMinFeret(k) = mean(minFerets) * pixelSize;
    end
     
 end

So I am novice to matlab, some others in the group before me were working on this but left adn now i have to fix some errors i am getting. I run into this wall because the larger loop is supposed to be for a particular area in the image but then i have another loop that indixes for the boundaries found within that area but it never results successfully such that the features within that area with those boundaries are indexed properly. What do you suggest to correct this?

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Chanille 2023-4-6

在 MATLAB Online 中打开

1_controlforimageanalysis.jpg

    BI = imread(MyRGBImage,1); %%AI channel or (MyRGBImage); for other images
    bg = imopen(BI, strel('disk', 25));
    
    
    imgNoBg = BI - bg;
    imgContrast = imadjust(imgNoBg); 

I haven't finished the rest of the code because I am trying to fix the above issue but it would be something like this:

    %%  % Display average region properties for current image
    fprintf('Avg Area: %f, Avg Circularity: %f, Avg Feret Diameter: %f, Num Objects: %d\n', ...
        avgArea, avgCircularity, avgFeretDiam, numObjects);

Chanille 2023-4-6

@Image Analyst I still have a question even after reading your really cool demo. How can i ensure that the boundaries detected are what the regionprops is calculating within my for k loop? Thank you! I look forward to contributing to this forum some day. :)

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Answer 1

Image Analyst 2023-4-6

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1942819-i-have-boundaries-of-objects-in-my-code-i-want-to-use-those-boundaries-to-calculate-the-features-of#answer_1210994

在 MATLAB Online 中打开

You don't need that inside loop over size(B, 1). You can simply get all the values like

% Calculate region properties for each connected component in this zone
statscc = regionprops(cc, 'Area', 'Centroid', 'Eccentricity', 'Perimeter', 'MajorAxisLength', 'MinorAxisLength','Circularity');
meanArea = mean([statscc.Area])
xyCentroids = vertcat(statscc.Centroid); % Not sure what you want to do with these.
meanEccentricity = mean([statscc.Eccentricity])
meanMajorAxisLength = mean([statscc.MajorAxisLength])
meanMinorAxisLength = mean([statscc.MinorAxisLength])
meanCircularity = mean([statscc.Circularity])
% and so on.

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Image Analyst 2023-4-7

Your statement(s) is/are very vague. Not sure what "use the features as a basis to create a code that also measures the features" means. I don't do that -- use features to measure features. What we do is to use some algorithm to process the image in a way such that you can threshold it to get objects of interest, and background. I call this the mask and this process is called image segmentation. Sometimes you can threshold right away but sometimes you must do something to the image first before you can threshold it. And sometimes after you threshold the blobs in the mask still need some processing to extract only the blobs you're interested in and not the others. After all that is done you will have what we call a "segmented image". You then simply pass it to regionprops to make the measurements you want. Those measurements are often called "features" - things like area, perimeter, mean intensity, etc.

Now to classify your object(s) or image, you must put those features into a classification routine. If you want, you can manually look at the measurements/features and decide on what class, for example look at the area to classify blobs into big blobs and small blobs, or look at circularity to classify into round or non-round blobs. Sometimes it's not so simple like that and you're not sure what features should be compared and how. So in that case, if you have the Statistics and Machine Learning Toolbox you can put all of your features into columns of a table called something like tPredictors, and your "true" classes (known in advance because you have a training set) into the "responses" and use the "Classification Learner" app on the Apps tab of the tool ribbon. It will create a model object (variable) which you can then pass to a function, along with your unknown image, to classify it. There is a field of the trainedModel that will tell you how to classify your image with your new trainedModel.

You did not say what the classes were so I can't help much until you say what classes you want to sort your images into.

Image Analyst 2023-4-9

在 MATLAB Online 中打开

features.mat

I meant the code with the fitcsvm in it, not the image analysis program. Anyway this is what I have:

s = load('features.mat')
s = struct with fields:
    features: [1×3 struct]
features = s.features
features = 1×3 struct array with fields:
    filename
    totalArea
    avgSize
    zoneArea
    AvgCircularityy
    AvgFeret
    AvgMinFeret
    profileCounts
tbl = struct2table(features)
tbl = 3×8 table
                                          filename                                           totalArea       avgSize       zoneArea      AvgCircularityy     AvgFeret      AvgMinFeret    profileCounts
    ____________________________________________________________________________________    ___________    ___________    ___________    _______________    ___________    ___________    _____________

    {'1_Merge_WD_M1_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif'    }    1×12 double    1×12 double    1×12 double      1×12 double      1×12 double    1×12 double     1×12 double 
    {'2_8bit_Merge_WD_M1_12pm_dend12_actin647_lipitox594.lif - TileScan 1_Merged-2.tif'}    1×12 double    1×12 double    1×12 double      1×12 double      1×12 double    1×12 double     1×12 double 
    {'3_Merge_WD_M1_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif'    }    1×12 double    1×12 double    1×12 double      1×12 double      1×12 double    1×12 double     1×12 double 

Now, in order to classify that, with fitcsvm or ClassificationLearner, what we need is the "true" classes for each image. Then we need to figure out why there are 12 measurements for each image, and if east set of those has the same class or different classes.

Image Analyst 2023-4-10

If you're going to train a classifier, you have to have some images that have known classes, like class A, class B, and Class C or whatever. Then might represent density of particles, how effective the drug was, how rough the material is, the time stability of the material, or whatever it is. How do you expect a ton of numbers to predict what class it is if you don't even know? So you must know. And since you have 12 zones per image we need to know if the class of each zone is the same (for your ground truth images) or if they can have different classes. You must assign the known, true, ground truth class for each zone and put the data for each zone into the predictor table for your classifier training into one row of the table, as if each zone were its own image. So let's say Class A = "stable over time" and class B = "corrodes rapidly". So for each zone in one image you'd need to know the classes, like maybe they're all A or all B or maybe they go like this [AAABBBAAAABAB]. That's what you have to specify.

Image Analyst 2023-4-11

编辑：Image Analyst 2023-4-11

I feel like we're speaking different languages. Your featuresclassA.mat does not have ground truth class assignments in it. It's just a collection of measurements made on each image. Is each filename in that mat file a class? Like file "1_control.tif" is "drug worked perfectly", file "2_control.tif" is class "drug worked slightly", and file "3_control.tif" is "drug did not work at all"? Again measurements are measurements, not ground truth. Do you not have classes defined, and you just have a multidimensional space and you believe you have clusters there, and you want to find out the cluster definitions but you don't know how many clusters you have? There are ways of using kmeans to figure out what the best number of clusters is, in case you don't know for certain.

I don't understand your class definitions. Do you have two classes/types of images:

one class of images that are "advanced non-restricted class" and
another set of images that could be called "unknown class 2"?

If so, that's fine but we'll need a bunch of images in each class to train a classifier. You can't do it with just one image of each type. Since 7-dimensional space is impossible to visualize (though we can handle it mathematically), let's use a 2-D analogy. Let's say you had two features: var1 and var2. Now if you plotted those in 2-D you'd have two dots in the x-y graph. And one dot would be class1 and the other dot would be in class 2. But that does not make a good classifier. Let's say you used that model and another image came in with var1 and var 2 being some totally different values than your training set. So now you'd have three dots on your graph. That new, third dot -- which class should it be assigned to? Who can say with just two training points. That's why you need lots of training points.

If you do have two classes like I said above, how do the images of each class look? Is there some obvious noticeable visual difference? If so, show us. There might not be, and that's OK. With 7 features it may be difficult to see a visual difference.

Or, wait for @Walter Roberson

Image Analyst 2023-4-11

编辑：Image Analyst 2023-4-11

OK, you have measurements from 6 images now, but how many classes do you have? Two? Three? For each of the 6 images, what class are they in? Even one feature is enough. 7 is plenty. But we just need to know what is the "true" class for each of the images? The features are representative of a class but they are not the class number itself.

For example let's say you had 1 feature - the average gray level of the whole image. And let's say you had two classes: "bright images" and "dark images". Now you might have a set of 100 images with all their average brightnesses, but that does not say what class they are in. Perhaps your definition of dark images is where the mean gray level is less than 100. So knowing that you can figure out what class it is. With 7 features, you don't know how to classify them -- if you did you wouldn't need to train a classifier so you must have some other trusted way of saying what class each image is. Perhaps it's just your visual judgment, but whatever it it, you need to have the classes as the "true response" to use in @doc:classificationLearner

Image Analyst 2023-4-11

编辑：Image Analyst 2023-4-11

在 MATLAB Online 中打开

CLASSES.xlsx

@Chanille the workbook has three sheets for class A, B, and C. I assume each row contains the measurements from a different file but I'm not sure. What do rows represent? Also there seem to be 10 or 11 feature measurements in each row but I don't know what they are or how they relate to the 7 features (for each zone) that you had before. We can work with that data and just assume that there are 10 features. Here is the code:

% Read in table ta from sheet 'ClassA'.
ta = readtable('CLASSES.xlsx', 'Sheet', 'ClassA');
% Take just Columns 3 to the end
ta = ta(:, 3:end);
% Read in table tb from sheet 'ClassB'.
tb = readtable('CLASSES.xlsx', 'Sheet', 'ClassB');
% Take just Columns 3 to the end
tb = tb(:, 3:end);
% Combine the tables
tPredictors = [ta; tb];
% Create a vector that has the true classes.  Class 1 for the top rows (A) and 2 for the lower rows (B)
trueClasses = [ones(height(ta), 1); 2 * ones(height(tb), 1)]
% Setup a session
classificationLearner(tPredictors, trueClasses)

and after running Classification Learner with all models you can see the KNN model is the best:

and the SVM is second best:

I had to delete the extra column you put into the ClassA tab to make sure they had the same variable names and number of columns. That new workbook is attached. If you had 3 classes, you'd just extend the concept in the obvious way.

Chanille 2023-4-11

在 MATLAB Online 中打开

@Image Analyst

I especially like the your code is not that long, I am not even sure the code I made would work as well yours:

% Load the image data and labels
load('image_data.mat');
load('labels.mat');
% Extract HOG features from the image data
cell_size = 8;
hog_features = extractHOGFeatures(image_data, 'CellSize', [cell_size cell_size]);
% Select the top 50 principal components
num_components = 50;
[coeff, score, latent] = pca(hog_features);
selected_features = score(:, 1:num_components);
% Split the selected features into training and testing sets
[train_data,test_data,train_labels,test_labels] = ...
    crossvalind('HoldOut', labels, 0.3);
train_features = selected_features(train_data, :);
test_features = selected_features(test_data, :);
% Train the K-means classifier
num_clusters = 10; % Number of clusters
kmeans_classifier = fitckmeans(train_features, num_clusters);
% Predict the labels for the testing set using the K-means classifier
predicted_labels = predict(kmeans_classifier, test_features);
% Calculate the accuracy of the classifier
accuracy = sum(predicted_labels == test_labels) / numel(test_labels);
% Display the accuracy of the classifier
disp(['The accuracy of the classifier is: ', num2str(accuracy)]);
% Extract HOG features from the new image
new_image = imread('new_image.jpg');
new_hog_features = extractHOGFeatures(new_image, 'CellSize', [cell_size cell_size]);
% Project the new features onto the selected principal components
new_selected_features = (new_hog_features * coeff(:, 1:num_components));
% Predict the class of the new image using the trained K-means classifier
new_label = predict(kmeans_classifier, new_selected_features);
% Display the predicted label of the new image
disp(['The predicted label of the new image is: ', num2str(new_label)]);

Chanille 2023-4-12

编辑：Chanille 2023-4-12

在 MATLAB Online 中打开

T2.xlsx

@Image Analyst After I generated the function from the most accurate model, I tried to use new data that i know belongs to either Class A or Class B to see if the classifer would classify the new data corectly.

I uploaded the table with this code on a new compiler tab:

T2 = readtable('T2.xlsx', 'Sheet', 'Sheet1');
yfit = trainedClassifier.predictFcn(T2); 
%%Generated fucntion
function [trainedClassifier, validationAccuracy] = trainClassifier(trainingData, responseData)
% [trainedClassifier, validationAccuracy] = trainClassifier(trainingData,
% responseData)
% Returns a trained classifier and its accuracy. This code recreates the
% classification model trained in Classification Learner app. Use the
% generated code to automate training the same model with new data, or to
% learn how to programmatically train models.
%
%  Input:
%      trainingData: A table containing the same predictor columns as those
%       imported into the app.
%
%      responseData: A vector with the same data type as the vector
%       imported into the app. The length of responseData and the number of
%       rows of trainingData must be equal.
%
%  Output:
%      trainedClassifier: A struct containing the trained classifier. The
%       struct contains various fields with information about the trained
%       classifier.
%
%      trainedClassifier.predictFcn: A function to make predictions on new
%       data.
%
%      validationAccuracy: A double containing the accuracy as a
%       percentage. In the app, the Models pane displays this overall
%       accuracy score for each model.
%
% Use the code to train the model with new data. To retrain your
% classifier, call the function from the command line with your original
% data or new data as the input arguments trainingData and responseData.
%
% For example, to retrain a classifier trained with the original data set T
% and response Y, enter:
%   [trainedClassifier, validationAccuracy] = trainClassifier(T, Y)
%
% To make predictions with the returned 'trainedClassifier' on new data T2,
% use
%   yfit = trainedClassifier.predictFcn(T2)
%
% T2 must be a table containing at least the same predictor columns as used
% during training. For details, enter:
%   trainedClassifier.HowToPredict
% Auto-generated by MATLAB on 12-Apr-2023 08:32:47
% Extract predictors and response
% This code processes the data into the right shape for training the
% model.
inputTable = trainingData;
predictorNames = {'Var3', 'Var4', 'Var5', 'Var6', 'Var7', 'Var8', 'Var9', 'Var10', 'Var11', 'Var12'};
predictors = inputTable(:, predictorNames);
response = responseData;
isCategoricalPredictor = [false, false, false, false, false, false, false, false, false, false];
% Train a classifier
% This code specifies all the classifier options and trains the classifier.
classificationNeuralNetwork = fitcnet(...
    predictors, ...
    response, ...
    'LayerSizes', 100, ...
    'Activations', 'relu', ...
    'Lambda', 0, ...
    'IterationLimit', 1000, ...
    'Standardize', true, ...
    'ClassNames', [1; 2]);
% Create the result struct with predict function
predictorExtractionFcn = @(t) t(:, predictorNames);
neuralNetworkPredictFcn = @(x) predict(classificationNeuralNetwork, x);
trainedClassifier.predictFcn = @(x) neuralNetworkPredictFcn(predictorExtractionFcn(x));
% Add additional fields to the result struct
trainedClassifier.RequiredVariables = {'Var10', 'Var11', 'Var12', 'Var3', 'Var4', 'Var5', 'Var6', 'Var7', 'Var8', 'Var9'};
trainedClassifier.ClassificationNeuralNetwork = classificationNeuralNetwork;
trainedClassifier.About = 'This struct is a trained model exported from Classification Learner R2022a.';
trainedClassifier.HowToPredict = sprintf('To make predictions on a new table, T, use: \n  yfit = c.predictFcn(T) \nreplacing ''c'' with the name of the variable that is this struct, e.g. ''trainedModel''. \n \nThe table, T, must contain the variables returned by: \n  c.RequiredVariables \nVariable formats (e.g. matrix/vector, datatype) must match the original training data. \nAdditional variables are ignored. \n \nFor more information, see <a href="matlab:helpview(fullfile(docroot, ''stats'', ''stats.map''), ''appclassification_exportmodeltoworkspace'')">How to predict using an exported model</a>.');
% Extract predictors and response
% This code processes the data into the right shape for training the
% model.
inputTable = trainingData;
predictorNames = {'Var3', 'Var4', 'Var5', 'Var6', 'Var7', 'Var8', 'Var9', 'Var10', 'Var11', 'Var12'};
predictors = inputTable(:, predictorNames);
response = responseData;
isCategoricalPredictor = [false, false, false, false, false, false, false, false, false, false];
% Perform cross-validation
partitionedModel = crossval(trainedClassifier.ClassificationNeuralNetwork, 'KFold', 5);
% Compute validation predictions
[validationPredictions, validationScores] = kfoldPredict(partitionedModel);
% Compute validation accuracy
validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');

Image Analyst 2023-4-14

I want to do my own training. So, can you give me

The table of predictor measurements (training set)
The vector of "true" class numbers for those measurements in the training set
The table of "test" measurements (that we will use to predict)
The vector of "true" classes for the training set so we can see how well the predicted class matches up with the "true/known" class values.

I'm pretty sure you can test your test values (those not involved in making the model) right there in the Classification Learner. The "hold outs" used in training (like 20% of your training values) are included in your training data and are called "validation data" and the data not used in training or validating your model are called "test data". So you have 3 data sets involved, or actually 2. There is your "predictor" data from which it automatically splits into "training" and "validation" sets, and then there is your "test" set (which has never been seen during training and model building).

I hope this explains the terminology better.

Chanille 2023-4-15

@Image Analyst Thanks for the clarification I understand the terminology very well now.

Chanille 2023-4-17

@Image Analyst I'll start a new thread for this last question. Scrolling is very long here!

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I have boundaries of objects in my code. I want to use those boundaries to calculate the features of those objects in my preceding loop. please help fix

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I have boundaries of objects in my code. I want to use those boundaries to calculate the features of those objects in my preceding loop. please help fix

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