1,721,141 research outputs found
Nonrigid registration using a rigidity constraint
No full textNonrigid registration is a technique commonly used in the field of medical imaging. A drawback of most current nonrigid registration algorithms is that they model all tissue as being nonrigid. When a nonrigid registration is performed, the rigid objects in the image, such as bony structures or surgical instruments, may also transform nonrigidly. Other consequences are that tumour growth between follow-up images may be concealed, or that structures containing contrast material in one image and not in the other may be compressed by the registration algorithm. In this paper we propose a novel regularisation term, which is added to the cost function in order to penalise nonrigid deformations of rigid objects. This regularisation term can be used for any representation of the deformation field capable of modelling locally rigid deformations. By using a B-spline representation of the deformation field, a fast algorithm can be devised. We show on 2D synthetic data, on clinical CT slices, and on clinical DSA images, that the proposed rigidity constraint is successful, thus improving registration results
Projection-slice theorem based 2D-3D registration
No full textIn X-ray guided procedures, the surgeon or interventionalist is dependent on his or her knowledge of the patient's specific anatomy and the projection images acquired during the procedure by a rotational X-ray source. Unfortunately, these X-ray projections fail to give information on the patient's anatomy in the dimension along the projection axis. It would be very profitable to provide the surgeon or interventionalist with a 3D insight of the patient's anatomy that is directly linked to the X-ray images acquired during the procedure. In this paper we present a new robust 2D-3D registration method based on the Projection-Slice Theorem. This theorem gives us a relation between the pre-operative 3D data set and the interventional projection images. Registration is performed by minimizing a translation invariant similarity measure that is applied to the Fourier transforms of the images. The method was tested by performing multiple exhaustive searches on phantom data of the Circle of Willis and on a post-mortem human skull. Validation was performed visually by comparing the test projections to the ones that corresponded to the minimal value of the similarity measure. The Projection-Slice Theorem Based method was shown to be very effective and robust, and provides capture ranges up to 62 degrees. Experiments have shown that the method is capable of retrieving similar results when translations are applied to the projection images
Voxel-based discriminant map classification on brain ventricles for Alzheimer's disease.
No full textOne major hallmark of the Alzheimer’s disease (AD) is the loss of neurons in the brain. In many cases, medical experts use magnetic resonance imaging (MRI) to qualitatively measure the neuronal loss by the shrinkage or enlargement of the structures-of-interest. Brain ventricle is one of the popular choices. It is easily detectable in clinical MR images due to the high contrast of the cerebro-spinal fluid (CSF) with the rest of the parenchyma. Moreover, atrophy in any periventricular structure will directly lead to ventricle nlargement. For quantitative analysis, volume is the common choice. However, volume is a gross measure and it cannot capture the entire complexity of the anatomical shape. Since most existing shape descriptors are complex and difficult-to-reproduce, more straightforward and robust ways to extract ventricle shape features are preferred in the diagnosis. In this paper, a novel ventricle shape based classification method for Alzheimer’s disease is proposed. Training process has to be performed to generate two probability maps for two training classes: healthy controls (HC) and AD patients. By subtracting of the HC obability map from the AD probability map, we get a 3D ventricle discriminant map. Then a atching coefficient has been calculated between each training subject and the discriminant map. An adjustable cut-off point of the matching coefficients has been drawn for the two asses. Generally, the higher the cut-off point has been drawn, the higher specificity can be achieved. However, it will result in relatively lower sensitivity and vice versa. The benchmarked results against volume based classification show that the area under the ROC curves for our proposed method is as high as 0.86 compared with only 0.71 for volume based classification method
A comparison of acceleration techniques for nonrigid medical image registration
No full textMutual information based nonrigid registration of medical images is a popular approach. The coordinate mapping that relates the two images is found in an iterative optimisation procedure. In every iteration a computationally expensive evaluation of the mutual information's derivative is required. In this work two acceleration strategies are compared. The first technique aims at reducing the number of iterations, and, consequently, the number of derivative evaluations. The second technique reduces the computational costs per iteration by employing stochastic approximations of the derivatives. The performance of both methods is tested on an artificial registration problem, where the ground truth is known, and on a clinical problem involving low-dose CT scans and large deformations. The experiments show that the stochastic approximation approach is superior in terms of speed and robustness. However, more accurate solutions are obtained with the first technique. © Springer-Verlag Berlin Heidelberg 2006
Improving 3D Active Appearance Model Segmentation of the Left Venticle with Jacobian Tuning
No full textTopological analysis of 3D cell nuclei using finite element template-based spherical mapping.
No full textTopological analysis of cells and subcellular structures on the basis of image data is one of the major trends in modern quantitative biology. However, due to the dynamic nature of cell biology, the optical appearance of different cells or even time series of the same cell is undergoing substantial variations in shape and texture which makes the analysis of image data a non-trivial task. In the absence of canonical invariances, a natural approach to the normalization of cell images consists in dimension reduction of the 3D problem by means of spherical mapping which enables the analysis of targeted regions in terms of radial distances. In this work, we present a finite element template-based approach for physically-base spherical mapping which has been applied for topological analysis of confocal laser scanning microscopy images of cell nuclei
Medical imaging 2007 : Image processing : 18-20 February 2007, San Diego, California, USA
No full text- …
