Strange Artifacts Detection on chest radiography
Abstract
Chest radiography is one of recommended imaging test by World Health Organization for childhood pneumonia diagnosis. However, during patient examination is very usual finding artifacts in these images, such as identification labels, fingerprints, shirt buttons, and so forth. Moreover, when these images are digitally scanned, other problems raise such as noise, brightness control and so on. Artifacts can reveal private data and expose patient identification. Furthermore, these artifacts can significantly damage automatic analysis by computer diagnosis aided systems. This works presents an efficient method for artifact identification composed by 3 main stages: histogram based pixel filtering, edge detection with Roberts algorithm and standard deviation spacial filtering. This method has been experimented upon 200 images database and presented about 7ms of time processing per image. Visually inspection was used to error measuring and we achieve 0.98 of precision. As a result of this, the method demonstrate a very promising preprocessing tool.
Text in Portuguese, Original Title: Deteccao de artefatos estranhos em radiografias de torax.
Fabrizzio Soares
Associate Professor and CS Chair
Fabrizzio Soares is a professor of Computer Science, Information Systems and Software Engineering at INF/UFG. His research interests include Computer Vision, Human Computer Interaction, Machine Learning and Programming topics. He is the leader of the Pixellab group, which develops solutions for accesibilty, Precision Agriculture, and Interactive Systems.