Bone Fracture Identification in X-Ray Images using Fuzzy Wavelet Features

The fracture detection process is difficult and requires specialized knowledge of the anatomical structures of the area under consideration. X-ray imaging provides images of the body's internal structures. Despite the rapid developments of medical imaging by adding newer imaging techniques such as CT and MRI, the exam of choice to detect bone fractures faster and cheaper is x-ray imaging (radiography). The objective of this study is the automatic detection of fractures in bone x-ray images using an image classification method. The dataset that was used in this study consists of 300 x-ray bone images of upper and lower extremity. In this study, we propose a novel feature extraction and classification methodology for the detection of bone fractures, named Wavelet Fuzzy Phrases (WFP). WFP extracts textural information from different bands of the 2D Discrete Wavelet Transform (DWT) images, which is expressed by a set of words. Each word is represented by a fuzzy set. The words form phrases, obtained from the aggregation of the fuzzy sets, representing the image contents. The classification accuracy achieved for bone fracture detection is 84%, which is higher than that obtained by other, state-of-the-art bone fracture detection methods. The results of this work show that this method can be used to draw the attention of the physicians in areas of the x-rays that are suspicious for fracture; therefore, it could contribute in the reduction of diagnostic errors as well as the increase of the radiologists' productivity.