Home > Articles > Published articles > Diagnostically lossless coding of X-ray angiography images based on background suppression |
Date: | 2016 |
Abstract: | X-ray angiography images are widely used to identify irregularities in the vascular system. Because of their high spatial resolution and the large amount of images generated daily, coding of X-ray angiography images is becoming essential. This paper proposes a diagnostically lossless coding method based on automatic segmentation of the focal area using ray-casting and α-shapes. The diagnostically relevant Region of Interest is first identified by exploiting the inherent symmetrical features of the image. The background is then suppressed and the resulting images are encoded using lossless and progressive lossy-to-lossless methods, including JPEG-LS, JPEG2000, H. 264 and HEVC. Experiments on a large set of X-ray angiography images suggest that our method correctly identifies the Region of Interest. When compared to the case of coding with no background suppression, the method achieves average bit-stream reductions of nearly 34% and improvements on the reconstruction quality of up to 20 dB-SNR for progressive decoding. |
Grants: | Ministerio de Economía y Competitividad TIN/2015-71126-R Ministerio de Economía y Competitividad TIN/2012- 38102-C03-03 Agència de Gestió d'Ajuts Universitaris i de Recerca 2014/SGR-691 |
Rights: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. |
Language: | Anglès |
Document: | Article ; recerca ; Versió sotmesa a revisió |
Subject: | X-ray angiography images ; Diagnostically lossless coding ; Ray casting segmentation ; Alpha-shapes filters ; Region of interest compression |
Published in: | Computers and electrical engineering, Available online 03 March 2016, ISSN 0045-7906 |
Pre-print 10 p, 11.3 MB |