Localized Fisher vector representation for pathology detection in chest radiographs.

In this work, we present a novel framework for automatic detection of abnormalities in chest radiographs. The representation model is based on the Fisher Vector encoding method. In the representation process, we encode each chest radiograph using a set of extracted local descriptors. These include localized texture features that address typical local texture abnormalities as well as spatial features. Using a Gaussian Mixture Model, a rich image descriptor is generated for each chest radiograph. An improved representation is obtained by selection of features that correspond to the relevant region of interest for each pathology. Categorization of the X-ray images is conducted using supervised learning and the SVM classifier. The proposed system was tested on a dataset of 636 chest radiographs taken from a real clinical environment. We measured the performance in terms of area (AUC) under the receiver operating characteristic (ROC) curve. Results show an AUC value of 0.878 for abnormal mediastinum detection, and AUC values of 0.827 and 0.817 for detection of right and left lung opacities, respectively. These results improve upon the state-of-the-art as compared with two alternative representation models.