TY - GEN
T1 - Tissue volume and vesselness measure preserving nonrigid registration of lung CT images
AU - Cao, Kunlin
AU - Ding, Kai
AU - Christensen, Gary E.
AU - Reinhardt, Joseph M.
PY - 2010
Y1 - 2010
N2 - In registration-based analyses of lung biomechanics and function, high quality registrations are essential to obtain meaningful results. Various criteria have been suggested to find the correspondence mappings between two lung images acquired at different levels of inflation. In this paper, we describe a new metric, the sum of squared vesselness measure difference (SSVMD), that utilizes the rich information of blood vessel locations and matches similar vesselness patterns in two images. Preserving both the lung tissue volume and the vesselness measure, a registration algorithm is developed to minimize the sum of squared tissue volume difference (SSTVD) and SSVMD together. We compare the registration accuracy using SSTVD + SSVMD with that using SSTVD alone by registering lung CT images of three normal human subjects. After adding the new SSVMD metric, the improvement of registration accuracy is observed by landmark error and fissure positioning error analyses. The average values of landmark error and fissure positioning error are reduced by about 30% and 25%, respectively. The mean landmark error is on the order of 1 mm. Statistical testing of landmark errors shows that there is a statistically significant difference between two methods with p values < 0.05 in all three subjects. Visual inspection shows there are obvious accuracy improvements in the lung regions near the thoracic cage after adding SSVMD.
AB - In registration-based analyses of lung biomechanics and function, high quality registrations are essential to obtain meaningful results. Various criteria have been suggested to find the correspondence mappings between two lung images acquired at different levels of inflation. In this paper, we describe a new metric, the sum of squared vesselness measure difference (SSVMD), that utilizes the rich information of blood vessel locations and matches similar vesselness patterns in two images. Preserving both the lung tissue volume and the vesselness measure, a registration algorithm is developed to minimize the sum of squared tissue volume difference (SSTVD) and SSVMD together. We compare the registration accuracy using SSTVD + SSVMD with that using SSTVD alone by registering lung CT images of three normal human subjects. After adding the new SSVMD metric, the improvement of registration accuracy is observed by landmark error and fissure positioning error analyses. The average values of landmark error and fissure positioning error are reduced by about 30% and 25%, respectively. The mean landmark error is on the order of 1 mm. Statistical testing of landmark errors shows that there is a statistically significant difference between two methods with p values < 0.05 in all three subjects. Visual inspection shows there are obvious accuracy improvements in the lung regions near the thoracic cage after adding SSVMD.
KW - Image registration
KW - Lung CT images
KW - Pulmonary
KW - Vesselness measure
UR - http://www.scopus.com/inward/record.url?scp=79751494195&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79751494195&partnerID=8YFLogxK
U2 - 10.1117/12.844541
DO - 10.1117/12.844541
M3 - Conference contribution
AN - SCOPUS:79751494195
SN - 9780819480248
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2010
T2 - Medical Imaging 2010: Image Processing
Y2 - 14 February 2010 through 16 February 2010
ER -