Application of Structural and Textural Features from X-ray Images to Predict the Type of Bone Fracture Treatment

Analysis of medical images plays a very important role in clinical decisionmaking. For a long time it has required extensive involvement of a human expert. However, recent progress in data mining techniques, especially in machine learning, allows for creating decision models and support systems that help to automatize this task and provide clinicians with patient-specific therapeutic and diagnostic suggestions. In this paper, we describe a study aimed at building a decision model (a classifier) that would predict the type of treatment (surgical vs. non-surgical) for patients with bone fractures based on their X-ray images. We consider two types of features extracted from images (structural and textural) and used them to construct multiple classifiers that are later evaluated in a computational experiment. Structural features are computed by applying the Hough transform, while textural information is obtained from gray-level occurrence matrix (GLCM). In research reported by other authors structural and textural features were typically considered separately. Our findings show that while structural features have better predictive capabilities, they can benefit from combining them with textural ones. Interestingly, there are no statistical differences in overall classification accuracy attained by the classifiers considered in the study (it ranges from 91.0% to 96.1%), however, the most promising one is the random forest.