Development of the technique for automatic highlighting ranges of interest in lungs x-ray images

In this paper, information technology has been developed for highlighting ranges of interest in lung x-ray images, based on the calculation of textural properties and classification of k-means. In some cases, the highlighted objects can describe not only the current patient’s condition but also specific characteristics regarding age, gender, constitution, etc. While using the k-means method, the relationship between the segmentation error and fragmentation window size was revealed. Within the study, both a visual criterion for evaluating the quality of the segmentation result and a criterion based on calculating the clustering error on a large set of fragmented images were implemented. The study also included image pre-processing techniques. Thus, the study showed that the technology provided key objects highlighting error at 26%. However, the equalizing procedure has lessened this error to 14%. X-ray image clustering errors for fragmentation windows of 12x12, 24x24 and 36x36 were presented.