Multigradient field-active contour model for multilayer boundary detection of ultrasound rectal wall image

Extraction and reconstruction of rectal wall structures from an ultrasound image is helpful for surgeons in rectal clinical diagnosis and 3-D reconstruction of rectal structures from ultrasound images. The primary task is to extract the boundary of the muscular layers on the rectal wall. However, due to the low SNR from ultrasound imaging and the thin muscular layer structure of the rectum, this boundary detection task remains a challenge. An active contour model is an effective high-level model, which has been used successfully to aid the tasks of object representation and recognition in many image-processing applications. We present a novel multigradient field active contour algorithm with an extended ability for multiple-object detection, which overcomes some limitations of ordinary active contour models—"snakes." The core part in the algorithm is the proposal of multigradient vector fields, which are used to replace image forces in kinetic function for alternative constraints on the deformation of active contour, thereby partially solving the initialization limitation of active contour for rectal wall boundary detection. An adaptive expanding force is also added to the model to help the active contour go through the homogenous region in the image. The efficacy of the model is explained and tested on the boundary detection of a ring-shaped image, a synthetic image, and an ultrasound image. The experimental results show that the proposed multigradient field-active contour is feasible for multilayer boundary detection of rectal wal

Model computation and matching with the neuractive pyramid

This paper introduces the neuractive pyramids to model objects under elastic deformations . One pyramid is built on each frame of the image sequence . Each level of the pyramid is a regular graph that is recursively built on a low-pass version of the original picture . Cells of this graph deform to model the local information of the picture . Deformations are obtained by the minimization of an energy function computed both on the gradient of the picture and the graph structure . Each cell contains a vector of statistical moments computed on its domain and its neighborood . Matching pyramids defines a vector Field of local elastic transformations . The matching operator is based on a self-organizing map, introduced by Kohonen . Softness and multiresolution aspects of the pyramids allow accurate and robust results . An application to the matching of 2D cardiac MRI scans shows the interest of the method for deformable objects .Cet article présente les pyramides neuractives pour la modélisation des objets subissant des déformations élastiques. Une pyramide est construite pour chaque image de la séquence. Chaque niveau de la pyramide est un graphe régulier construit récursivement sur l'image de départ convoluée par un filtre passe-bas. Les cellules de ce graphe s'adaptent au contenu local des images. L'adaptation du graphe est obtenue en minimisant une fonction énergétique basée sur le gradient de l'image et la déformation des cellules. Chaque cellule reçoit un vecteur de moments statistiques calculé sur sa zone d'intérêt et celles de ses voisines. La mise en correspondance des pyramides permet d'accéder au champ de vecteurs des transformations élastiques locales. L'algorithme de mise en correspondance est fondé sur une approche neuronale auto-organisatrice. La souplesse et l'aspect multirésolution des structures permettent d'obtenir des résultats robustes et précis. L'application à des images cardiaques obtenues par résonance magnétique (IRM) 2D montre l'intérêt de la méthode pour la modélisation d'objets complexes

Human skull shape and masticatory induced stress : objective comparison through the use of non-rigid registration

Variation in masticatory induced stress, caused by shape changes in the human skull, is quantified in this article. A comparison on masticatory induced stress is presented subject to a variation in human skull shape. Non-rigid registration is employed to obtain appropriate computational domain representations. This procedure allows the isolation of shape from other variations that could affect the results. An added benefit, revealed through the use of non-rigid registration to acquire appropriate domain representation, is the possibility of direct and objective comparison and manipulation. The effect of mapping uncertainty on the direct comparison is also quantified. As shown in this study, exact difference values are not necessarily obtained, but a non-rigid map between subject shapes and numerical results gives an objective indication on the location of differences.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2040-7947ai201

The Development And Application Of A Statistical Shape Model Of The Human Craniofacial Skeleton

Biomechanical investigations involving the characterization of biomaterials or improvement of implant design often employ finite element (FE) analysis. However, the contemporary method of developing a FE mesh from computed tomography scans involves much manual intervention and can be a tedious process. Researchers will often focus their efforts on creating a single highly validated FE model at the expense of incorporating variability of anatomical geometry and material properties, thus limiting the applicability of their findings. The goal of this thesis was to address this issue through the use of a statistical shape model (SSM). A SSM is a probabilistic description of the variation in the shape of a given class of object. (Additional scalar data, such as an elastic constant, can also be incorporated into the model.) By discretizing a sample (i.e. training set) of unique objects of the same class using a set of corresponding nodes, the main modes of shape variation within that shape class are discovered via principal component analysis. By combining the principal components using different linear combinations, new shape instances are created, each with its own unique geometry while retaining the characteristics of its shape class. In this thesis, FE models of the human craniofacial skeleton (CFS) were first validated to establish their viability. A mesh morphing procedure was then developed to map one mesh onto the geometry of 22 other CFS models forming a training set for a SSM of the CFS. After verifying that FE results derived from morphed meshes were no different from those obtained using meshes created with contemporary methods, a SSM of the human CFS was created, and 1000 CFS FE meshes produced. It was found that these meshes accurately described the geometric variation in human population, and were used in a Monte Carlo analysis of facial fracture, finding past studies attempting to characterize the fracture probability of the zygomatic bone are overly conservative

Feature-Based Models for Three-Dimensional Data Fitting.

There are numerous techniques available for fitting a surface to any supplied data set. The feature-based modeling technique takes advantage of the known, geometric shape of the data by deforming a model having this generic shape to approximate the data. The model is constructed as a rational B-spline surface with characteristic features superimposed on its definition. The first step in the fitting process is to align the model with a data set using the center of mass, principal axes and/or landmarks. Using this initial orientation, the position, rotation and scale parameters are optimized using a Newton-type optimization of a least squares cost function. Once aligned, features embedded within the model, corresponding to pertinent characteristics of the shape, are used to improve the fit of the model to the data. Finally, the control vertex weights and positions of the rational B-spline model are optimized to approximate the data to within a specified tolerance. Since the characteristic features are defined within the model a creation, important measures are easily extracted from a data set, once fit. The feature-based modeling approach is demonstrated in two-dimensions by the fitting of five facial, silhouette profiles and in three-dimensions by the fitting of eleven human foot scans. The algorithm is tested for sensitivity to data distribution and structure and the extracted measures are tested for repeatability and accuracy. Limitations within the current implementation, future work and potential applications are also provided

Image processing using the Walsh transform.

This thesis presents a new algorithm which can be used to register images of the same or different modalities e.g images with multiple channels such as X-rays, temperature, or elevation or simply images of different spectral bands. In particular, a correlation-based scheme is used, but instead of grey values, it correlates numbers formulated by different combinations of the extracted local Walsh coefficients of the images. Each image patch is expanded in terms of Walsh basis functions. Each Walsh basis function can be thought of as measuring a different aspect of local structure, eg horizontal edge, corner, etc. The coefficients of the expansion, therefore, can be thought of as dense local features, estimating at each point the degree of presence of, for example, a horizontal edge, a corner with contrast of a certain type, etc. These coefficients are normalised and used as digits in a chosen number system which allows one to create a unique number for each type of local structure. The choice of the basis of the number system allows one to give different emphasis to different types of local feature (e.g. corners versus edges) and thus the method we present forms a unified framework in terms of which several feature matching methods may be interpreted. The algorithm is compared with wavelet based approaches, using simulated and real images. The images used for the registration experiments are assumed to differ from each other by a rotation and a translation only. Additionally, the method was extended to cope with 3D image sets, while as an add-on, it was also tried in performing image segmentation

Algorithmic assessment of cardiac viability using magnetic resonance imaging

MRI is a non-invasive imaging method which produces high resolution images of human tissues from inside the human body. Due to its outstanding ability, it is quickly becoming a major tool for medical and clinical studies, including high profile areas such as neurology, oncology, cardiology and etc. MRI technology developed relatively slowly compared to other methods such as x-ray. A decade ago, it took more than 5 minutes to construct an MR image. However more recently, with several significant inventions such as echo planar imaging and steady state free procession techniques, the acquisition time of MRI has significantly reduced. At present, it is possible to capture dozens of MR images in a second. Those techniques are generally called ultra-fast MRI. The fast MR acquisition techniques enable us to extend our studies to the moving tissues such as the myocardium. Using the ultra-fast MRI, multiple images can be acquired during a cardiac cycle allowing the construction of cardiac cinematographic MR images. Cardiac motion can therefore be revealed. Abnormal cardiac motion is often related to cardiac diseases such as ischaemic myocardium and myocardial infarction. With advanced MRI techniques, cardiac diseases can be more specifically defined. For example, the late contrast enhanced MRI highlights acute myocardial infarction. The first-pass perfusion MRI suggests the existence of ischaemic myocardium. At the present time the majority of the analysis of MR images can be performed either qualitatively or quantitatively. The qualitative assessment is an eye-ball assessment of the images on a MRI workstation, which is subjective and inaccurate. The quantitative assessment of MR image relies on the computer technologies of both hardware and software. In recent years, the demands for the quantitative assessment of MR images have increased sharply. Many so-called computer aided diagnosis systems were developed to process data either more accurately or more efficiently. In this study, we developed an algorithmic method to analyse the late contrast enhanced MR images, revealing the so-called hibernating myocardium. The algorithm is based on an efficient and robust image registration algorithm. Using the image registration algorithm, we are able to integrate the static late contrast enhanced MR image with its corresponding cardiac cinematography MR images, and so constructing cardiac CINE late enhanced MR images. Our algorithm was tested on 20 subjects. In each of the subject, the mean left ventricle diastolic volume and systolic volume was measured by planimetry from both the original CINE images and the constructed late enhanced CINE images. The results are: left ventricle diastolic volume (original / constructed) = 206 / 215 ml, p = 0.35. Left ventricle systolic volume (original / constructed) = 129 / 123 ml, p = 0.33. With our algorithm, the cardiac motion and the myocardial infarction can therefore be studied simultaneously to locate the hibernating myocardium which moves abnormally. The accurate location of the hibernating myocardium is important because it could turn into the irreversible myocardial infarction. On the other hand, with proper medical treatment or cardiac surgery, the hibernating myocardium could be revitalised. The experimental results show there are no significant differences between the artificial cine late contrast enhanced MR images and the original cinematography MR images in left ventricle diastolic volume, left ventricle systolic volume. The method therefore appears promising as an improved cardiac viability assessment tool. In addition, we extended the method to a semi-automatic cardiac contour definition algorithm, which has produced a satisfactory result in contour definition for cardiac cinematography MR images from 34 subjects including 20 healthy volunteers and 14 patients. Although it is a semi-automatic method, the diagnosis time could be significantly reduced compared to the manual method. The algorithm was preliminarily tested on 10 first-pass perfusion MR sequences and 10 aortic MR sequences. The experimental results were satisfactory. Although, minor manual correction is required on some occasions, we believe our method could be clinically useful for the study of cardiac cinematography MR images, first-pass perfusion MR images and aortic MR images