TY - JOUR
T1 - JPEG2000 ROI coding through component priority for digital mammography
AU - Bartrina-Rapesta, Joan
AU - Serra-Sagrist, Joan
AU - Aulí-Llins, Francesc
PY - 2011/1/1
Y1 - 2011/1/1
N2 - Region Of Interest (ROI) coding is a prominent feature of some image coding systems aimed to prioritize specific areas of the image through the construction of a codestream that, decoded at increasing bit-rates, recovers the ROI first and with higher quality than the rest of the image. JPEG2000 is a wavelet-based coding system that is supported in the Digital Imaging and Communications in Medicine (DICOM) standard. Among other features, JPEG2000 provides lossy-to-lossless compression and ROI coding, which are especially relevant to the medical community. But, due to JPEG2000 supported ROI coding methods that guarantee lossless coding are not designed to achieve a high degree of accuracy to prioritize ROIs, they have not been incorporated in the medical community. This paper introduces a ROI coding method that is able to prioritize multiple ROIs at different priorities, guaranteeing lossy-to-lossless coding. The proposed ROI Coding Through Component Prioritization (ROITCOP) method uses techniques of rate-distortion optimization combined with a simple yet effective strategy of ROI allocation that employs the multi-component support of JPEG2000 codestream. The main insight in ROITCOP is the allocation of each ROI to an component. Experimental results indicate that this ROI allocation strategy does not penalize coding performance whilst achieving an unprecedented degree of accuracy to delimit ROIs. The proposed ROITCOP method maintains JPEG2000 compliance, thus easing its use in medical centers to share images. This paper analyzes in detail the use of ROITCOP to mammographies, where the ROIs are identified by computer-aided diagnosis. Extensive experimental tests using various ROI coding methods suggest that ROITCOP achieves enhanced coding performance. © 2010 Published by Elsevier Inc.
AB - Region Of Interest (ROI) coding is a prominent feature of some image coding systems aimed to prioritize specific areas of the image through the construction of a codestream that, decoded at increasing bit-rates, recovers the ROI first and with higher quality than the rest of the image. JPEG2000 is a wavelet-based coding system that is supported in the Digital Imaging and Communications in Medicine (DICOM) standard. Among other features, JPEG2000 provides lossy-to-lossless compression and ROI coding, which are especially relevant to the medical community. But, due to JPEG2000 supported ROI coding methods that guarantee lossless coding are not designed to achieve a high degree of accuracy to prioritize ROIs, they have not been incorporated in the medical community. This paper introduces a ROI coding method that is able to prioritize multiple ROIs at different priorities, guaranteeing lossy-to-lossless coding. The proposed ROI Coding Through Component Prioritization (ROITCOP) method uses techniques of rate-distortion optimization combined with a simple yet effective strategy of ROI allocation that employs the multi-component support of JPEG2000 codestream. The main insight in ROITCOP is the allocation of each ROI to an component. Experimental results indicate that this ROI allocation strategy does not penalize coding performance whilst achieving an unprecedented degree of accuracy to delimit ROIs. The proposed ROITCOP method maintains JPEG2000 compliance, thus easing its use in medical centers to share images. This paper analyzes in detail the use of ROITCOP to mammographies, where the ROIs are identified by computer-aided diagnosis. Extensive experimental tests using various ROI coding methods suggest that ROITCOP achieves enhanced coding performance. © 2010 Published by Elsevier Inc.
KW - Digital mammogram compression
KW - JPEG2000 standard
KW - Rate-distortion optimization
KW - Region Of Interest coding
UR - https://ddd.uab.cat/record/144682
U2 - https://doi.org/10.1016/j.cviu.2010.09.008
DO - https://doi.org/10.1016/j.cviu.2010.09.008
M3 - Article
VL - 115
SP - 59
EP - 68
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
SN - 1077-3142
IS - 1
ER -