The aim of image registration is to align two or more images taken from different viewpoints, at different time
instances, or by different modalities. Image registration methods are divided into two main categories, feature based
and intensity based methods. Recently intensity based methods have gained popularity since they aim at finding a
dense correspondence between the images needed to be aligned without calculating correspondence between salient
features.
In this work, a new intensity based image registration method has been proposed and tested. This method models the
source and target image as a single image displaced over time and calculates the optical flow fields in a
multiresolution framework. In order to have the ability to represent complex fields, the deformation has been
modelled as locally affine but globally smooth. Multiresolution image representation by steerable pyramid
decomposition is integrated with the differential image registration technique in order to find accurate image
deformations. The usage of steerable pyramid overcomes traditional problems in other pyramidal methods namely
aliasing across different bands, lack of translation and rotation invariance.
The new algorithm was validated using torso images for volunteers at the University of Alberta in addition to
images captured of a cast model of the human torso. Experiments have demonstrated promising results in terms of
root mean square error and average pixel error.
Medical experts often examine hundreds of spine x-rays to determine existence of diseases like
osteoarthritis, osteoporoses, and osteophites. Accurate vertebrae segmentation plays a great role in the
proper assessment of various vertebral abnormalities. Manual segmentation methods are both time
consuming and non-reproducible, hence, developing efficient computer-assisted segmentation methods has
been a long standing goal. Over the past decade, segmentation methods that utilize statistical models of
shape and appearance have drawn much interest within the medical imaging community. However, despite
being a promising approach, they are always faced with a number of challenges such as: poor image
quality, and the ability to generalize well to unseen vertebral deformities.
This paper presents a novel vertebral segmentation method using Contourlet-based salient point matching
and a localized multi-scale shape prior. We employ a multi-scale directional analysis tool, namely
contourlets, to build local appearance profiles at salient points of the vertebra's body. The contourlet-based
local appearance model is used to detect the vertebral bodies in the test x-ray image. A novel localized
multi-scale shape prior is used to drive the segmentation process. Within a best-basis selection framework,
the proposed shape prior benefits from the multi-scale nature of wavelet packets, and the capability of ICA
to capture hidden independent modes of variations. Experiments were conducted using a set of 100 digital
x-ray images of lumbar spines. The contourlet-based appearance profiles and the localized multi-scale
shape prior were constructed using a training subset of images, and then matched to unseen images.
Promising results were obtained compared to related work in the literature with an average segmentation
error of 1.1997 mm.
Statistical models of deformations are becoming crucial tools for a variety of computer vision applications such as
regularization and validation of image registration and segmentation algorithms. In this article, we propose a new
approach to effectively represent the statistical properties of high dimensional deformations. In particular, we propose
techniques that use independent component analysis (ICA) in conjunction with wavelet packet decomposition. Two
different architectures for ICA have been investigated; one treats the elastic deformations as random variables and the
individual deformation field as outcomes and a second which treats the individual deformations as random variables
and the elastic deformations as outcomes. The experiments were conducted using the Amsterdam Library of Images
(ALOI), and the proposed algorithms were evaluated using the model generalization as a statistical measure.
Experimental results show a significant improvement when compared to a recent deformation representation in the
literature.
Radiographs of the spine are frequently examined for assessment of vertebral abnormalities. Features like
osteophytes (bony growth of vertebra's corners), and disc space narrowing are often used as visual
evidence of osteoarthris or degenerative joint disease. These symptoms result in remarkable changes in the
shapes of the vertebral body. Statistical analysis of anatomical structure has recently gained increased
popularity within the medical imaging community, since they have the potential to enhance the automated
diagnosis process. In this paper, we present a novel method for computer-assisted vertebral classification
using a localized, pathology-related shape model. The new classification scheme is able to assess the
condition of multiple vertebrae simultaneously, hence is possible to directly classify the whole spine
anatomy according to the condition of interest (anterior osteophites). At the core of this method is a new
localized shape model that uses concepts of sparsity, dimension reduction, and statistical independence to
extract sets of localized modes of deformations specific to each of the vertebrae under investigation. By
projection of the shapes onto any specific set of deformation modes (or basis), we obtain low-dimensional
features that are most directly related to the pathology of the vertebra of interest. These features are then
used as input to a support vector machine classifier to classify the vertebra under investigation as normal or
upnormal. Experiments are conducted using contours from digital x-ray images of five vertebrae of lumbar
spine. The accuracy of the classification scheme is assessed using the ROC curves. An average specifity of
96.8 % is achieved with a sensitivity of 80 %.
Image registration is the process of aligning two images taken from different views, at different times, or by different
modalities. In this article, we propose a new framework that incorporates prior deformation knowledge in the
registration process. First, an elastic image registration method is used to obtain deformation fields by modeling the
nonrigid deformations as locally affine and globally smooth flow fields. Next, the estimated geometric transformation
maps are used to train a prior deformation model using two subspace projection techniques, namely principle
component analysis (PCA) and independent component analysis (ICA). A smooth deformation is now guaranteed by
projecting the locally calculated deformation onto a subspace of allowed deformations. One advantage of our approach
is in its ability to guarantee smoothness without the need for iterative regularization. The new algorithms were validated
using the Amsterdam library of images (ALOI). Our experiments demonstrate promising results in terms of mean square
error.
Statistical shape priors try to faithfully represent the full range of biological variations in anatomical structures. These
priors are now widely used to restrict shapes; obtained in applications like segmentation and registration; to a subspace
of plausible shapes. Principle component analysis (PCA) is commonly used to represent modes of shape variations in a
training set. In an attempt to face some of the limitations in the PCA-based shape model, this paper describes a new
multi-scale shape prior using independent component analysis (ICA) and adaptive wavelet decomposition. Within a
best basis selection framework, the proposed method benefits from the multi-scale nature of wavelet packets, and the
capability of ICA to capture higher order statistics in wavelet subspaces. The proposed approach is evaluated using
contours from digital x-ray images of five vertebrae of human spine. We demonstrate the ability of the proposed shape
prior to capture both local and global shape variations, even with limited number of training samples. Our results also
show the performance gains of the ICA-based analysis for the wavelet sub-spaces, as compared to PCA-based analysis
approach.
With the increased emphasis on security and personal authentication, an accurate biometric-based authentication system
has become a critical requirement in a variety of applications. Among different biometrics, authentication based on iris
features has received a lot of attention since its introduction in 1992. The wavelet transform has been proposed by
several researchers for extracting iris features for authentication. Although classical wavelets provide a good
performance, they suffer from limited orientation selectivity. In this paper, we investigate the potentials of using the
contourlet transform to represent the iris texture. A new iris representation and matching system based on contourlet
transform is proposed. The contourlet transform not only shares the multiscale and localization properties of wavelets,
but also has a higher degree of directionality and anisotropy. The proposed matching system is experimented in both
verification and identification modes. Results have shown the significance of the new technique, especially in case of
low quality iris images and highly security demanding applications.
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