📄️ No Classification
If no classification is selected, the image is not classified and thus, the labels that have been drawn in the image are not changed but used directly in later post processing and information calculation.
🗃️ Deep Learning
3 items
📄️ Threshold
Threshold classification assigns pixels to labels based on predefined value ranges. For each label, you specify the minimum and maximum values that a pixel may have for one or more image features.
📄️ Bayesian
Bayesian classification assigns pixels or objects to classes based on probability. Using the training labels, the method learns the typical characteristics of each class. It then calculates which class is the most likely match for each pixel or object.
📄️ Decision forest
A Decision Forest classifier assigns pixels or objects to classes by combining the results of multiple decision trees. Each decision tree makes a classification based on the training labels and the measured image features, such as staining intensity, color, or texture. The forest combines these individual decisions to select the most likely class.
📄️ K-means clustering
K-means clustering groups pixels according to similarities in their feature values, such as staining intensity, color, or other measured image properties. Pixels with similar values are placed in the same group, or cluster.
📄️ Detect Region Of Interest
Detect Region of Interest is a simple method that divides the image into sections, based on pixel intensity. This method is a useful tool for separation of the background and tissue.
📄️ Cell classification
Cell Classification is a useful tool for detecting individual cells. Ensure that a number of image classes for training have been created and labelled according to cell types in the images. Recall that more training labels, means that more data will be used to train the classifier. Thus, drawing many training labels, leads to a better performing classifier. Also recall that all image classes defined need to have drawn training labels of its type.