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9 March 2010 A comparison of basic deinterlacing approaches for a computer assisted diagnosis approach of videoscope images
Andreas Kage, Marcia Canto, Emmanuel Gorospe, Antonio Almario, Christian Münzenmayer
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Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 76243K (2010) https://doi.org/10.1117/12.844366
Event: SPIE Medical Imaging, 2010, San Diego, California, United States
Abstract
In the near future, Computer Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support clinical experts in the diagnosis of images derived from imaging modalities such as endoscopy. In the recent past, a few first approaches for computer assisted endoscopy have been presented already. These systems use a video signal as an input that is provided by the endoscopes video processor. Despite the advent of high-definition systems most standard endoscopy systems today still provide only analog video signals. These signals consist of interlaced images that can not be used in a CAD approach without deinterlacing. Of course, there are many different deinterlacing approaches known today. But most of them are specializations of some basic approaches. In this paper we present four basic deinterlacing approaches. We have used a database of non-interlaced images which have been degraded by artificial interlacing and afterwards processed by these approaches. The database contains regions of interest (ROI) of clinical relevance for the diagnosis of abnormalities in the esophagus. We compared the classification rates on these ROIs on the original images and after the deinterlacing. The results show that the deinterlacing has an impact on the classification rates. The Bobbing approach and the Motion Compensation approach achieved the best classification results in most cases.
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Andreas Kage, Marcia Canto, Emmanuel Gorospe, Antonio Almario, and Christian Münzenmayer "A comparison of basic deinterlacing approaches for a computer assisted diagnosis approach of videoscope images", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76243K (9 March 2010); https://doi.org/10.1117/12.844366
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KEYWORDS
Video
Databases
Image resolution
Endoscopy
Image classification
Computer aided diagnosis and therapy
Esophagus
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