Meningioma subtype classification using morphology features and random forests

The majority of meningiomas belong to one of four subtypes: fibroblastic, meningothelial, transitional and psammomatous. Classification of histopathology images of these meningioma is a time consuming and error prone task, and as such automatic methods aim to help reduce time spent and errors made. This work is concerned with classifying histopathology images into the above subtypes by extracting simple morphology features to represent each image subtype. Morphology features are identified based on the pathology of the meningioma subtypes and are used to classify each image into one of the four WHO Grade I subtypes. The morphology features correspond to visual changes in the appearance of cells, and the presence of psammoma bodies. Using morphological image processing these features can be extracted and the presence of each detected feature is used to build a vector for each meningioma image. These feature vectors are then classified using a Random Forest based classifier. A set of 80 images was used for experimentation with each subtype being represented by 20 images, and a ten-fold cross validation approach was used to obtain an overall classification accuracy. Using the above methodology a maximum classification accuracy of 91:25% is achieved across the four subtypes with coherent misclassification (e.g. no misclassification between fibroblastic and meningothelial). This work demonstrates that morphology features can be used to perform meningioma subtype classification and provide an understandable link between the features identified in the images and the classification results obtained.