TY - GEN
T1 - Watermarked cardiac CT image segmentation using deformable models and the Hermite transform
AU - Gomez-Coronel, Sandra L.
AU - Moya-Albor, Ernesto
AU - Escalante-Ramírez, Boris
AU - Brieva, Jorge
N1 - Publisher Copyright:
© 2015 SPIE.
PY - 2015
Y1 - 2015
N2 - Medical image watermarking is an open area for research and is a solution for the protection of copyright and intellectual property. One of the main challenges of this problem is that the marked images should not differ perceptually from the original images allowing a correct diagnosis and authentication. Furthermore, we also aim at obtaining watermarked images with very little numerical distortion so that computer vision tasks such as segmentation of important anatomical structures do not be impaired or affected. We propose a preliminary watermarking application in cardiac CT images based on a perceptive approach that includes a brightness model to generate a perceptive mask and identify the image regions where the watermark detection becomes a difficult task for the human eye. We propose a normalization scheme of the image in order to improve robustness against geometric attacks. We follow a spread spectrum technique to insert an alphanumeric code, such as patient's information, within the watermark. The watermark scheme is based on the Hermite transform as a bio-inspired image representation model. In order to evaluate the numerical integrity of the image data after watermarking, we perform a segmentation task based on deformable models. The segmentation technique is based on a vector-value level sets method such that, given a curve in a specific image, and subject to some constraints, the curve can evolve in order to detect objects. In order to stimulate the curve evolution we introduce simultaneously some image features like the gray level and the steered Hermite coefficients as texture descriptors. Segmentation performance was assessed by means of the Dice index and the Hausdorff distance. We tested different mark sizes and different insertion schemes on images that were later segmented either automatic or manual by physicians.
AB - Medical image watermarking is an open area for research and is a solution for the protection of copyright and intellectual property. One of the main challenges of this problem is that the marked images should not differ perceptually from the original images allowing a correct diagnosis and authentication. Furthermore, we also aim at obtaining watermarked images with very little numerical distortion so that computer vision tasks such as segmentation of important anatomical structures do not be impaired or affected. We propose a preliminary watermarking application in cardiac CT images based on a perceptive approach that includes a brightness model to generate a perceptive mask and identify the image regions where the watermark detection becomes a difficult task for the human eye. We propose a normalization scheme of the image in order to improve robustness against geometric attacks. We follow a spread spectrum technique to insert an alphanumeric code, such as patient's information, within the watermark. The watermark scheme is based on the Hermite transform as a bio-inspired image representation model. In order to evaluate the numerical integrity of the image data after watermarking, we perform a segmentation task based on deformable models. The segmentation technique is based on a vector-value level sets method such that, given a curve in a specific image, and subject to some constraints, the curve can evolve in order to detect objects. In order to stimulate the curve evolution we introduce simultaneously some image features like the gray level and the steered Hermite coefficients as texture descriptors. Segmentation performance was assessed by means of the Dice index and the Hausdorff distance. We tested different mark sizes and different insertion schemes on images that were later segmented either automatic or manual by physicians.
KW - Deformable models
KW - Hermite transform
KW - Perceptive watermarking
KW - Texture segmentation
UR - http://www.scopus.com/inward/record.url?scp=84922971790&partnerID=8YFLogxK
U2 - 10.1117/12.2073432
DO - 10.1117/12.2073432
M3 - Contribución a la conferencia
AN - SCOPUS:84922971790
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - 10th International Symposium on Medical Information Processing and Analysis
A2 - Romero, Eduardo
A2 - Lepore, Natasha
PB - SPIE
T2 - 10th International Symposium on Medical Information Processing and Analysis
Y2 - 14 October 2014 through 16 October 2014
ER -