Synergies between texture features: an abstract feature based framework for meningioma subtypes classification

Histopathology is the gold standard for accurate diagnosis of cancer, tumors and similar diseases. Real-world pathological images, due to non-homogeneous nature and unorganized spatial intensity variations, are complex to analyze and classify. The major challenge in classifying pathological images is the complexity due to high intra-class variability and low inter-class variation in texture. Accuracy of histopathological image classification is highly dependent on the relevancy of the selected features to the problem. This paper is an effort in the same direction and presents an abstract feature based framework called abstract feature framework (AFF) to select optimal set of the most relevant features to classify pathological images. An abstract feature is created by identifying interlinked run-length texture features and grouping them. AFF is comprised of a new data structure called Abstract Feature Tree (AFT) and an algorithm for manipulating it. AFT is a tree structure in which nodes are abstract features. The Linkage Learning Algorithm for manipulating AFT is the brain of this framework and inspired by genetic algorithm. It creates better abstract features by first identifying interlinked abstract features and then combining them. This process is repeated until no improvement is found. On termination, the final list of abstract features is used for classifying pathological images. The proposed framework was tested on real-world histopathological meningioma dataset. Results obtained proved that the proposed framework outperformed the best-known rank-based feature selection techniques by using, on average, approximately three times less features to achieve 22% higher classification accuracy.