Digital Image Processing
Model Supported Image Registration and Warping for Change Detection in Computer-aided Diagnosis
AIPR '00 Proceedings of the 29th Applied Imagery Pattern Recognition Workshop
Alignment by maximization of mutual information
Alignment by maximization of mutual information
A Framework for Automatic Segmentation of Lung Nodules from Low Dose Chest CT Scans
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
New motion correction models for automatic identification of renal transplant rejection
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Appearance models for robust segmentation of pulmonary nodules in 3d LDCT chest images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Precise segmentation of multimodal images
IEEE Transactions on Image Processing
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Our long term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. After equalizing signals for two subsequent chest scans, visual appearance of these chest images is described using a Markov-Gibbs random field (MGRF) model with multiple pairwise interaction. An affine transformation that globally registers a target to a prototype is estimated by the gradient ascent-based maximization of a special Gibbs energy function. To get an accurate visual appearance model, we developed a new approach to automatic selection of most characteristic second-order cliques that describe pairwise interactions in the LDCT data. To handle local deformations, we displace each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by a speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results on the 135 LDCT data sets from 27 patients show that the proposed accurate registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.