In medical image analysis the image content is often represented by features computed from the pixel matrix in order to support the development of improved clinical diagnosis systems. These features need to be interpreted and understood at a clinical level of understanding Many features are of abstract nature, as for instance features derived from a wavelet transform. The interpretation and analysis of such features are difficult. This lack of coincidence between computed features and their meaning for a user in a given situation is commonly referred to as the semantic gap. In this work, we propose a method for feature analysis and interpretation based on the simultaneous visualization of feature and image domain. Histopathological images of meningiomas WHO (World Health Organization) grade I are represented by features derived from color transforms and the Discrete Wavelet Transform. The wavelet-based feature space is then visualized and explored using unsupervised machine learning methods. We show how to analyze and select features according to their relevance for the description of clinically relevant characteristics.
Algorithms Artificial Intelligence