Performance of texture descriptors in classification of medical images with outsiders in database

During the years image classification gained important significance in practice, especially in the fields of digital radiology, remote sensing, image retrieval, etc. Typical algorithm for image classification contains descriptor extraction phase, learning phase and testing phase. Testing phase calculates accuracy of the classifier based on predetermined set of labelled images. This paper analyse performance of texture descriptors combined with SVMs, in the case when test dataset contains images not belonging to any predetermined class. A robustness of texture descriptors on outsiders is analysed, to see if descriptor is able to separate outsiders in specific class. Medical dataset containing various radiology images is used for testing. It was shown that it is possible to separate images not belonging to any class with cost of decreased performance by few percent.