Classification of radiological exams and organs by belief theory

The emergence of new medical image detectors such as multi-array scanners and the large diffusion of PET-scans has conditioned the modern medical practise. Indeed, the exams are more volumetric and more precise. We develop new medical software that allows us to classify exams. Coupled with a region of interest localization, it improves significantly the diagnosis time. The originality of our method resides in the application of the belief theory to medical image classification. The interest of belief theory lies in giving a formal representation of the inaccurate and uncertain aspect of information. Moreover, the concept of "extended open world" is well suited since it reduces the misclassification by introducing a class called "unknown". Two applications were performed: first, the exam type was identified among three major classes (ab domino-pelvic, cranial, and pulmonary). The others are gathered in the fourth special class "unknown". Second, according to a user request, the slices containing the specified organ are automatically selected.