Automatic extraction of multiple-study X-ray images (CROSBI ID 705761)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Dumenčić, Stella ; Tschauner, Sebastian ; Hržić, Franko ; Štajduhar, Ivan
engleski
Automatic extraction of multiple-study X-ray images
In medical radiology standard practice, radiographs of different projections or studies are occasionally merged into a single image for convenience. Before the radiographs can be utilized for further automatic processing, e.g., for detecting and/or localizing specific injuries, such merged projections or studies need to be separated. Doing this manually can be tiresome ; therefore, we decided to investigate the possibility of automating this process using contemporary image processing and machine learning techniques. We implemented two independent automatic solutions for this problem: (1) a manually-tailored image-splitting method using morphological operations, image binarization, and boundary-box detection, and (2) an instance of the YOLOv4 algorithm. The manually-tailored algorithm and YOLOv4 model were trained on the training data set of 4, 000 images and validated on the data set of 250 images. Comparing both on a disjoint test set, consisting of 250 images, the YOLOv4 model noticeably outperformed the manually-tailored method with an accuracy of 0.992, F1 score of 0.996, and the intersection-overunion of 0.8978±0.079. The results suggest that YOLOv4 can efficiently extract large portions of embedded projections or studies from medical radiographs.
object detection ; YOLO ; radiograph ; image segmentation
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
87
2021.
objavljeno
10.1109/INISTA52262.2021.9548551
Podaci o matičnoj publikaciji
978-1-6654-3603-8
Podaci o skupu
International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
predavanje
25.08.2021-27.08.2021
Köseköy, Turska