TY - GEN
T1 - Elastography of biological tissue
T2 - 7th International Conference on Image Analysis and Recognition, ICIAR 2010
AU - Antonio Sánchez, C.
AU - Drapaca, Corina S.
AU - Sivaloganathan, Sivabal
AU - Vrscay, Edward R.
PY - 2010
Y1 - 2010
N2 - In recent years, imaging techniques have been adapted to indirectly measure stiffness of biological tissues, with the hope of using this information to aid in detecting and classifying pathological regions. Several methods have been developed to convert a sequence of strain images into a single elasticity image, but most are based on assumptions that limit the local variability of stiffness in the estimate. In this paper, two direct inversion methods are introduced. The novelty of these methods is that they concurrently solve a system of differential equations for the stiffness, allowing for strong local variations. Some ideas regarding uniqueness of solutions, an issue that is ignored in existing works, are also presented. Preliminary numerical results show that by keeping the differential terms in the tissue model, the new inversion methods can more accurately determine the tissue's stiffness distribution.
AB - In recent years, imaging techniques have been adapted to indirectly measure stiffness of biological tissues, with the hope of using this information to aid in detecting and classifying pathological regions. Several methods have been developed to convert a sequence of strain images into a single elasticity image, but most are based on assumptions that limit the local variability of stiffness in the estimate. In this paper, two direct inversion methods are introduced. The novelty of these methods is that they concurrently solve a system of differential equations for the stiffness, allowing for strong local variations. Some ideas regarding uniqueness of solutions, an issue that is ignored in existing works, are also presented. Preliminary numerical results show that by keeping the differential terms in the tissue model, the new inversion methods can more accurately determine the tissue's stiffness distribution.
UR - http://www.scopus.com/inward/record.url?scp=77955395796&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-13775-4_20
DO - 10.1007/978-3-642-13775-4_20
M3 - Conference contribution
AN - SCOPUS:77955395796
SN - 3642137741
SN - 9783642137747
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 195
EP - 206
BT - Image Analysis and Recognition - 7th International Conference, ICIAR 2010, Proceedings
Y2 - 21 June 2010 through 23 June 2010
ER -