Biomedical subjects

Maria Petrou

Publications and source records attributed to Maria Petrou.

6 recordsLinked to original sources

PubMed2006

Image registration using the Walsh transform.

This paper 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 gray 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, e.g., 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 normalized 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 and contour based approaches, using simulated and real images. The two images are assumed to differ from each other by a rotation and a translation only.

Algorithms↗

PubMed2006

Three-dimensional nonlinear invisible boundary detection.

The human vision system can discriminate regions which differ up to the second-order statistics only. We present an algorithm designed to reveal "hidden" boundaries in gray level images, by computing gradients in higher order statistics of the data. We demonstrate it by applying it to the identification of possible "hidden" boundaries of glioblastomas as manifest themselves in three-dimensional (3-D) MRI scans, using a model driven approach. We also demonstrate the method using a nonmodel driven approach where we have no prior information about the location of possible boundaries. In this case, we use 3-D MRI data concerning schizophrenic patients and normal controls.

Algorithms↗

PubMed2006

Affine parameter estimation from the trace transform.

In this paper, we assume that we are given the images of two segmented objects, one of which may be an affinely distorted version of the other, and wish to recover the values of the parameters of the affine transformation between the two images. The images may also differ by the overall level of illumination. The multiplicative constant of such difference may also be recovered. We present a generic theoretical framework to solve this problem. In terms of this framework, other proposed methods may be interpreted. We show how, in this framework, one can recover the affine parameters in a way that is robust to various effects, such as occlusion and illumination variation. The proposed method is generic enough to be applicable also to matching two images that do not depict the same scene or object.

Algorithms↗

PubMed2006

The "invaders" algorithm: range of values modulation for accelerated correlation.

In this paper, we present an algorithm that allows the simultaneous calculation of several cross correlations. The algorithm works by shifting the range of values of different images/signals to occupy different orders of magnitude and then combining them to form a single composite image/signal. Because additional signals are placed in the space usually occupied by a single signal, we call this the "invaders algorithm," to imply that extra signals invade the space that normally belongs to a single signal. After correlation is performed, the individual results are recovered by performing the inverse operation. The limitations of the algorithm are imposed by the finite length of the mantissa of the hardware used, the precision of the algorithm that performs the cross correlation (e.g., the precision of the fast Fourier transform (FFT)) and by the actual values of the images/signals that are to be combined. The algorithm does not require any special hardware or special FFT algorithm. For typical 256 x 256 images, an acceleration by a factor of at least two in the calculation of their cross correlations is guaranteed using an ordinary PC or a laptop. As for smaller sized templates, tenfold accelerations may be achieved.

Algorithms↗

PubMed2004

Affine invariant features from the trace transform.

The trace transform is a generalization of the Radon transform that allows one to construct image features that are invariant to a chosen group of image transformations. In this paper, we propose a methodology and appropriate functionals that can be computed from the image function and which can be used to calculate features invariant to the group of affine transforms. We demonstrate the usefulness of the constructed image descriptors in retrieving images from an image database and compare it with relevant state-of-the-art object retrieval methods.

Algorithms↗

PubMed2003

Detection of structural differences between the brains of schizophrenic patients and controls.

This paper investigates the validity of the null hypothesis: there are no structural differences between the brains of schizophrenic and normal control subjects that manifest themselves in MRI-T(2) data and distinguish the two populations in a statistically significant way. The data used refer to 21 schizophrenic patients and 19 normal controls, matched for age, sex and social background. The methodology used is based on three-dimensional texture analysis, which is used to quantify anisotropy in the data at scales of the order of a few millimetres. These data reject the null hypothesis. In addition, this article attempts to identify the regions of the brain that are responsible for the morphological characteristics that distinguish the two populations. For this purpose, it utilises a second texture analysis method that, in spite of being a global method, allows one to trace back to the data the origin of the features that most distinctly distinguish the two populations. This method indicates that the features that distinguish the two populations with P values smaller than 10(-6) are located in the most inferior part of the brain and in particular in the tissue that makes up the sulci. It is stressed that in order to preserve the integrity of the data for texture calculations, no registration of anatomical structures is performed, and the most inferior part of the brain is identified as referring to those slices of the scans that visually correspond to slices 1-12 of the Talairach and Tournoux brain atlas.

Adult↗