User-defined B-Spline template snake

This thesis has created a new Snake model that overcomes many of the limitations of the traditional finite difference snake. This new deformable model combines a novel user initialization process with a finite element B-spline snake to create a powerful semi-automatic segmentation method. Using the simple but powerful initialization process, the user recognizes critical points and regions in a specified order, and transfers this knowledge to the model. By drawing lines across the object of interest, importatn information pertaining to the global shape of the object, such as width and symmetry, is imparted to the model. The snake is parameterized using minimum number of model degrees of freedom necessary and these degrees of freedom are placed in optimal positions around the object, based on the critical points and features recognized by the user via the input lines. Thus, the model is more like a deformable template than a local snake model - it is less sensitive to noise and more amenable to propagation to subsequent image slices in a volume image or time series. Unlike a traditional deformable template model however, it is constructed and positioned by the user rather than preconstructed and automatically initialized by the segmentation system. The template snake isinitialized very close to the object boundary and is very similar in shape. Furthermore, it is "aware" of its position with respect to the object. This thesis also describes the computation of the external image forces and how the known initial position and shape of the snake can be used to design object-specific image forces.

User-defined B-Spline template snake

This thesis has created a new Snake model that overcomes many of the limitations of the traditional finite difference snake. This new deformable model combines a novel user initialization process with a finite element B-spline snake to create a powerful semi-automatic segmentation method. Using the simple but powerful initialization process, the user recognizes critical points and regions in a specified order, and transfers this knowledge to the model. By drawing lines across the object of interest, importatn information pertaining to the global shape of the object, such as width and symmetry, is imparted to the model. The snake is parameterized using minimum number of model degrees of freedom necessary and these degrees of freedom are placed in optimal positions around the object, based on the critical points and features recognized by the user via the input lines. Thus, the model is more like a deformable template than a local snake model - it is less sensitive to noise and more amenable to propagation to subsequent image slices in a volume image or time series. Unlike a traditional deformable template model however, it is constructed and positioned by the user rather than preconstructed and automatically initialized by the segmentation system. The template snake isinitialized very close to the object boundary and is very similar in shape. Furthermore, it is "aware" of its position with respect to the object. This thesis also describes the computation of the external image forces and how the known initial position and shape of the snake can be used to design object-specific image forces.

Robot visual measurement and grasping strategy for roughcastings

To overcome the challenging problem of visual measurement and grasping of roughcasts, a visual grasping strategy for an industrial robot is designed and implemented on the basis of deep learning and a deformable template matching algorithm. The strategy helps realize the positioning recognition and grasping guidance for a metal blank cast in complex backgrounds under the interference of external light. The proposed strategy has two phases: target detection and target localization. In the target detection stage, a deep learning algorithm is used to recognize the combined features of the surface of an object for a stable recognition of the object in nonstructured environments. In the target localization stage, high-precision positioning of metal casts with an unclear contour is realized by combining the deformable template matching and LINE-MOD algorithms. The experimental results show that the system can accurately provide visual grasping guidance for robots.

Morphology library of nano-silica based on thermal induced deformable template

We report a versatile platform for the synthesis of shape-tunable nano-silica based on thermal induced deformable template with diverse morphologies ranging from spheres, horns, ultrathin nano-sheets, rings to belts. This...

Statistical Analysis of Sacral Bone Density using a CNN-based Atlas Creation on a Large-scale CT database

Fragility fracture of the sacrum has been an issue for elderly people. Research has been limited for the sacrum partly because its shape is complex with large inter-subject variation. Also, large-scale statistical analysis of its shape and density distribution has been limited mainly due to the computational load in establishment of the voxel correspondences, i.e., deformable registration.In this study, we employed a convolutional neural network (CNN)-based deformable registration algorithm in the analysis of the sacral bone. The algorithm we employed, VoxelMorph (Dalca et al. Med Image Anal 2019), is characterized as an unsupervised algorithm where no ground truth deformation field is required. The algorithm also allows to create a conditional deformable template, which is a volume image exhibiting smallest deformation field from all samples with specific attributes (e.g., age, sex, etc.), which in short represents the “average” (or “centroid”) image among the specific age and sex group. We applied it on a database consisting of 837 CTs (149 males, 688 females, 58.14 ± 14.73 y.o.) of the pelvis region, where the sacral bone was segmented and masked. We computed the templates corresponding to ages of 20 to 80 for male and female. The templates visually illustrated reduction of the bone density with aging in both male and female. The quantitative analysis showed that the average CT value over the sacrum region was reduced from 135.73 HU to 39.88 HU for 20 y.o. template to 80 y.o. template.

Timely-Automatic Procedure for Estimating the Endocardial Limits of the Left Ventricle Assessed Echocardiographically in Clinical Practice

In this paper, we propose an analytical rapid method to estimate the endocardial borders of the left ventricular walls on echocardiographic images for prospective clinical integration. The procedure was created as a diagnostic support tool for the clinician and it is based on the use of the anisotropic generalized Hough transform. Its application is guided by a Gabor-like filtering for the approximate delimitation of the region of interest without the need for computing further anatomical characteristics. The algorithm is applying directly a deformable template on the predetermined filtered region and therefore it is responsive and straightforward implementable. For accuracy considerations, we have employed a support vector machine classifier to determine the confidence level of the automated marking. The clinical tests were performed at the Cardiology Clinic of the County Emergency Hospital Timisoara and they improved the physicians perception in more than 50% of the cases. The report is concluded with medical discussions.