Peter Rogelj

University of Primorska | UP · Department of Information Sciences and Technologies

The fusion of electroencephalography (EEG) with machine learning is transforming rehabilitation. Our study introduces a neural network model proficient in distinguishing pre- and post-rehabilitation states in patients with Broca’s aphasia, based on brain connectivity metrics derived from EEG recordings during verbal and spatial working memory tasks...

Eyes open and eyes closed data is often used to validate novel human brain activity classification methods. The cross-validation of models trained on minimally preprocessed data is frequently utilized, regardless of electroencephalography data comprised of data resulting from muscle activity and environmental noise, affecting classification accurac...

In this paper, we propose a new method to study and evaluate the time-varying brain network dynamics. The proposed RICI-imCPCC method (relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient) is based on an adaptive window size and the imaginary part of the complex Pearson correlation...

The calculation of shear forces and bending moments are the basis of every ship design documentation. It is essential for determining the strength of the ship and the reliability of the structure itself. Nowadays, due to a sheer volume of data, this calculation is performed exclusively with the help of specialized software packages. Such shear forc...

In the background of all human thinking—acting and reacting are sets of connections between different neurons or groups of neurons. We studied and evaluated these connections using electroencephalography (EEG) brain signals. In this paper, we propose the use of the complex Pearson correlation coefficient (CPCC), which provides information on connec...

Mutual information (MI) is one of the most popular and widely used similarity measures in image registration. In traditional registration processes, MI is computed in each optimization step to measure the similarity between the reference image and the moving image. The presumption is that whenever MI reaches its highest value, this corresponds to t...

Magnetic resonance imaging has achieved an increasingly important role in the clinical work-up of renal diseases such chronic kidney disease (CKD). A large panel of parameters have been proposed to diagnose CKD among them total kidney volume (TKV) which recently qualified as biomarker. Volume estimation in renal MRI is based on image segmentation o...

In this study, we focus on improving the efficiency and accuracy of nonrigid multi-modality registration of medical images. In this regard, we analyze the potentials of using the point similarity measurement approach as an alternative to global computation of mutual information (MI), which is still the most renown multi-modality similarity measure....

The article Image registration in dynamic renal MRI-current status and prospects, written by Frank G. Zöllner, Amira Šerifović‑Trbalić, Gordian Kabelitz, Marek Kociński, Andrzej Materka and Peter Rogelj, was originally published electronically on the publisher's internet portal on 9 October 2019 without open access.With the author(s)' decision to o...

Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acq...

Consumers often need additional information to decide which products would best suit their needs. This information is in practice limited due to limited space, limits of human attention, large number of products, etc. On the other hand, any approach to provide any kind of information to the customer is effective only if it does not require excessiv...

This paper describes our application of a novel method in the field of traffic sign recognition - the D2 shape function. We first give an overview of the advances and research in this field. We then describe the D2 shape function that was originally used to classify 3D models of various objects - because of its robustness we propose its use in the...

Several methods that are currently used for contouring analysis have problems providing reliable and/or meaningful results. In this paper a solution to these problems is proposed in a form of a novel measure, which was developed based on requirements defined for contouring studies. The proposed distance deviation measure can be understood as an ext...

Background and aim: We aimed to quantify target volume delineation uncertainties in cervix cancer image guided adaptive brachytherapy (IGABT). Materials and methods: Ten radiation oncologists delineated gross tumour volume (GTV), high- and intermediate-risk clinical target volume (HR CTV, IR CTV) in six patients. Their contours were compared wit...

MRI sequences with short scanning times may improve accessibility of image guided adaptive brachytherapy (IGABT) of cervix cancer. We assessed the value of 3D MRI for contouring by comparing it to 2D multi-planar MRI. In 14 patients, 2D and 3D pelvic MRI were obtained at IGABT. High risk clinical target volume (HR CTV) was delineated by 2 experienc...

In our research we aim to reduce the computation time spent on the evaluation protocol of a criterion function for rigid registration tasks. The basic evaluation protocol is performed on N uniformly distributed sampling lines in the K-dimensional transformation space. Similarity between two images is measured at each of the equidistantly placed poi...

We have applied automated image analysis methods in the assessment of human kidney perfusion based on 3D dynamic contrast-enhanced MRI data. This approach consists of non-rigid 3D image registration of the moving kidney followed by k-means clustering of the voxel time courses with split between left and right kidney. This method was applied to four...

The aim of our research is to analyse the importance of texture information for registration of a DRR (Digital Reconstructed Radiograph) and EPI (Electronic Portal Image) medical images. In our research, texture features are extracted by Laws texture coefficients and used for computing registration criterion functions. The proposed feature based ap...

The aim of our research is to analyse the importance of texture information for registration of a DRR (digital reconstructed radiograph) and EPI (electronic portal image) medical images. In our research, texture features are extracted by Laws texture coefficients and used for computing registration criterion functions. The proposed feature based ap...

The aim of our research is to analyse the im-portance of texture information for affine registration of gray-scale far-infrared (FLIR) images and gray-scale im-ages taken in the visible spectrum. Texture features are extracted by Laws texture coefficients and used for com-puting registration criterion functions. The proposed fea-ture based approach...

In this paper we focus on motion correction of contrast enhanced kidney MRI time series, which is an important step towards accurate assessment of regional renal function. Due to respiratory motion and pulsations, the organ of interest undergoes complex movement and deformation, which disturb further renal function analysis. We propose geometric mo...

This paper is concerned with the problem of multi-modality registration of images with complex geometrical relationship. We propose a high-dimensional approach based on point similarity measures and symmetric registration concept combined with a convolution-based geometric model. The high-dimensional registration approach follows the principles of...

This paper presents an original non-rigid image registration approach, which tends to improve the registration by establishing a symmetric image interdependence. In order to gather more information about the image transformation it measures the image similarity in both registration directions. The presented solution is based on the interaction betw...

Spatial deformation models are used to regularize image registration such that they prevent physically and anatomically unlikely transformations. It is often assumed that optimal models are obtained by modeling deformation properties of real tissues. However, this is not exactly true, because external forces, which drive the registration, in genera...

Registration of multi-modality images requires similarity measures that can deal with complex and unknown image intensity dependencies.

The purpose of this study was to demonstrate the construction of voxelwise ventilation-perfusion (V/Q) ratio maps in a porcine model by nonrigidly aligning the respective ventilation and perfusion images using a multimodality registration algorithm. The first-pass contrast agent technique for a blood flow map and 3He used for ventilation imaging we...

High-dimensional non-rigid registration of multi-modal data requires similarity measures with two important properties: multi-modality and locality. Unfortunately all commonly used multi-modal similarity measures are inherently global and cannot operate on small image regions. In this paper, we propose a new class of multi-modal similarity measures...

Registration of multi-modality images requires similarity measures that can deal with complex and unknown image intensity dependencies. Such measures have to rely on statistics, and consequently, they require relatively large image regions to operate. This makes the detection of localized image discrepancies difficult. As a solution we propose poin...

Rigid registration is usually performed as an op-timization procedure that searches for an image transforma-tion that gives best similarity between the registered images. Similarity is used as a measure of image correspondence. In this work we present an implementation of rigid multi-modality registration based on point similarity measures, which w...

We describe the evaluation of a non-rigid image registration method for multi-modal data. The evaluation is made di#cult by the absence of gold standard test data, for which the true transformation from one image to another is known. Di#erent approaches have been used to deal with this deficiency, e.g., by using synthetically warped data, by compar...

Non-rigid multimodal registration requires similarity measure with two important properties: locality and multi- modality. Unfortunately all commonly used multimodal similarity measures are inherently global and cannot be directly used to estimate local image properties. We have derived a local similarity measure based on joint entropy, which can o...

In this paper we focus on local similarity measures based on Shannon entropy which can be used for multimodal image matching employing deformations. The advantage of our approach is that global similarity or similarity of a larger image region can be computed from the similarities of its constitutive parts or individual voxels. We also discuss the...

The aim of our research was to analyse some of the texture features extracted through Laws texture coefficients for the registration of a DRR (Digital Reconstructed Radiograph) and EPI (Electronic Portal Image) medical images. For this purpose we analysed the mutual information (MI) of the texture features obtained from the two 2D-images. The textu...