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Objective: The purpose of this study is to propose a complete methodology for automatically registering three-dimensional (3D) pre-operative and post-operative CT scan dental volumes as well as to provide a toolset for quantifying and evaluating their volumetric differences. Methods: The proposed methodology was applied to cone beam CT (CBCT) data from 20 patients in order to assess the volume of augmented bone in the alveolar region. In each case, the pre-operative and post-operative data were registered using a 3D affine-based scheme. The performance of the 3D registration algorithm was evaluated by measuring the average distance between the edges of the registered sets. The differences between the registered sets were assessed through 3D subtraction radiography. The volume of the differences was finally evaluated by defining regions of interest in each slice of the subtracted 3D data and by combining all respective slices to model the desired volume of interest. The effectiveness of the algorithm was verified by applying it to several reference standard-shaped objects with known volumes. Results: Satisfactory alignment was achieved as a low average offset of 1.483± 1.558mm was recorded between the edges of the registered sets. Moreover, the estimated volumes closely matched the volumes of the reference objects used for verification, as the recorded volume differences were less than 0.4 mm3 in all cases. Conclusion: The proposed method allows for automatic registration of 3D CBCT data sets and the volumetric assessment of their differences in particular areas of interest. The proposed approach provides accurate volumetric measurements in three dimensions, requiring minimal user interaction. [ABSTRACT FROM AUTHOR]

Contrast correction is often required in digital subtraction radiography when comparing medical data acquired over different time periods owing to dissimilarities in the acquisition process. This paper focuses on dental radiographs and introduces a novel approach for correcting the contrast in dental image pairs. The proposed method modifies the subject images by applying typical registration techniques on their histograms. The proposed histogram registration method reshapes the histograms of the two subject images in such a way that these images are matched in terms of their contrast deviation. The method was extensively tested over 4 sets of dental images, consisting of 72 registered dental image pairs with unknown contrast differences as well as 20 dental pairs with known contrast differences. The proposed method was directly compared against the well-known histogram-based contrast correction method. The two methods were qualitatively and quantitatively evaluated for all 92 available dental image pairs. The two methods were compared in terms of the contrast root mean square difference between the reference image and the corrected image in each case. The obtained results were also verified statistically using appropriate t-tests in each set. The proposed method exhibited superior performance compared with the well-established method, in terms of the contrast root mean square difference between the reference and the corrected images. After suitable statistical analysis, it was deduced that the performance advantage of the proposed approach was statistically significant [ABSTRACT FROM AUTHOR]

We present a combined study of the mechanical properties of 3D printed scaffolds made by nanocomposite materials based on polycaprolactone (PCL). The geometry and dimensions of the three different systems is the same. Τhe porosity is 50% for all systems. Distributions of von-Mises strains and stresses, and total deformations were obtained through Finite Element Analysis (FEA) for a maximum amount of force applied, in a compressive numerical experiment. Also compressive experiments were performed for both raw and 3D nanoconposite scaffolds.

Objective: Electroencephalographic analysis (EEG) has emerged as a powerful tool for brain state interpretation. Studies have shown distinct deviances of patients with schizophrenia in EEG activation at specific frequency bands., Methods: Evidence is presented for the validation of a Convolutional Neural Network (CNN) model using transfer learning for scalp EEGs of patients and controls during the performance of a speeded sensorimotor task and a working memory task. First, we trained a CNN on EEG data of 41 schizophrenia patients (SCZ) and 31 healthy controls (HC). Secondly, we used a pretrained model for training. Both models were tested in an external validation set of 15 SCZ, 16 HC, and 12 first-degree relatives., Results: Using the layer-wise relevance propagation on the classification decision, a heatmap was produced for each subject, specifying the pixel-wise relevance. The CNN model resulted in the first case in a balanced accuracy of 63.7% and 81.5% in the second case, on the external validation test 64.5% and 83.2%, respectively., Conclusions: The theta and alpha frequency bands of the EEG signals had significant relevance to the CNN classification decision and predict the first-degree relatives indicating potential heritable functional deviances., Significance: The proposed methodology results in important advancements for the identification of biomarkers in schizophrenia heritability., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Cardiovascular disease (CVD) is a major health problem throughout the world. It is the leading cause of morbidity and mortality and also causes considerable economic burden to society. The early symptoms related to previous observations and abnormal events, which can be subjectively acquired by self-assessment of individuals, bear significant clinical relevance and are regularly preserved in the patient's health record. The aim of our study is to develop a machine learning model based on selected CVD-related information encompassed in NHANES data in order to assess CVD risk. This model can be used as a screening tool, as well as a retrospective reference in association with current clinical data in order to improve CVD assessment. In this form it is planned to be used for mass screening and evaluation of young adults entering their army service. The experimental results are promising in that the proposed model can effectively complement and support the CVD prediction for the timely alertness and control of cardiovascular problems aiming to prevent the occurrence of serious cardiac events.

Contrast and brightness matching are often required in many medical imaging applications, especially when comparing medical data acquired over different time periods, due to dissimilarities in the acquisition process. Numerous methods have been proposed in this field, ranging from simple correction filters to more complicated recursive techniques. This paper presents a comprehensive comparison of five methods for matching the contrast and brightness of medical image pairs, namely, Contrast Stretching, Ruttimann's Robust Film Correction, Boxcar Filtering, Least-Squares Approximation and Histogram Registration. The five methods were applied to a total of 100 image pairs, divided into five sets, in order to evaluate the performance of the compared methods on images with different levels of contrast, brightness and combinational contrast and brightness variations. Qualitative evaluation was performed by means of visual assessment on the corrected images as well as on digitally subtracted images, in order to estimate the deviations relative to the reference data. Quantitative evaluation was performed by pair-wise statistical evaluation on all image pairs in terms of specific features of merit based on widely used metrics. Following qualitative and quantitative analysis, it was deduced that the Histogram Registration method systematically outperformed the other four methods in comparison in most cases on average., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

Objective: The purpose of this study is to propose a complete methodology for automatically registering three-dimensional (3D) pre-operative and post-operative CT scan dental volumes as well as to provide a toolset for quantifying and evaluating their volumetric differences., Methods: The proposed methodology was applied to cone beam CT (CBCT) data from 20 patients in order to assess the volume of augmented bone in the alveolar region. In each case, the pre-operative and post-operative data were registered using a 3D affine-based scheme. The performance of the 3D registration algorithm was evaluated by measuring the average distance between the edges of the registered sets. The differences between the registered sets were assessed through 3D subtraction radiography. The volume of the differences was finally evaluated by defining regions of interest in each slice of the subtracted 3D data and by combining all respective slices to model the desired volume of interest. The effectiveness of the algorithm was verified by applying it to several reference standard-shaped objects with known volumes., Results: Satisfactory alignment was achieved as a low average offset of 1.483 ± 1.558 mm was recorded between the edges of the registered sets. Moreover, the estimated volumes closely matched the volumes of the reference objects used for verification, as the recorded volume differences were less than 0.4 mm(3) in all cases., Conclusion: The proposed method allows for automatic registration of 3D CBCT data sets and the volumetric assessment of their differences in particular areas of interest. The proposed approach provides accurate volumetric measurements in three dimensions, requiring minimal user interaction.

Contrast correction is often required in digital subtraction radiography when comparing medical data acquired over different time periods owing to dissimilarities in the acquisition process. This paper focuses on dental radiographs and introduces a novel approach for correcting the contrast in dental image pairs. The proposed method modifies the subject images by applying typical registration techniques on their histograms. The proposed histogram registration method reshapes the histograms of the two subject images in such a way that these images are matched in terms of their contrast deviation. The method was extensively tested over 4 sets of dental images, consisting of 72 registered dental image pairs with unknown contrast differences as well as 20 dental pairs with known contrast differences. The proposed method was directly compared against the well-known histogram-based contrast correction method. The two methods were qualitatively and quantitatively evaluated for all 92 available dental image pairs. The two methods were compared in terms of the contrast root mean square difference between the reference image and the corrected image in each case. The obtained results were also verified statistically using appropriate t-tests in each set. The proposed method exhibited superior performance compared with the well-established method, in terms of the contrast root mean square difference between the reference and the corrected images. After suitable statistical analysis, it was deduced that the performance advantage of the proposed approach was statistically significant.

Objectives: In this paper, the enhanced hexagonal centre-based inner search (EHCBIS) algorithm, for automatic point correspondence, is proposed for dental image registration., Methods: The presented algorithm is incorporated within a general registration scheme, which is based on extracting a set of candidate points on the reference image, finding their corresponding points in the image to be transformed (float image) using the proposed algorithm and applying a suitable geometrical transformation towards automatic registration. The performance of the proposed algorithm is evaluated against three well-known methods for automatic correspondence, the self-organizing maps, the automatic extraction of corresponding points and the trimmed iterative closest point method, in terms of registration accuracy., Results: Qualitative and quantitative results on registering 123 dental pairs show that the proposed algorithm outperforms the other methods for automatic correspondence with or without the presence of noise., Conclusions: The EHCBIS method is capable of defining automatically corresponding points in dental image pairs. It can be incorporated within a general scheme for point-based registration of dental radiographs acquired with or without rigorous a priori standardization. The applied projective transformation provides a reliable model for registering intraoral radiographs. The methodology does not require any segmentation prior to alignment providing subtraction radiographs and fused images for clinical evaluation regarding the evolution of a disease or the response to a therapeutic scheme.

In this paper, the relation between retinal vessel diameters and glaucoma by means of advanced image processing techniques is examined. Eighteen (18) patients with ocular hypertension who converted to early glaucoma (converters) and nineteen (19) patients with ocular hypertension who remain stable (non-converters) were imaged with a fundus camera at regular intervals. The first acquired image serves as the reference image, which is compared to each follow-up image. The proposed methodology involves the extraction of vessel centerlines by means of differential calculus, the geometrical alignment (registration) of the images using the chamfer matching algorithm, the estimation of vessel diameters by fitting a Gaussian function to intensity profiles. The statistical analysis of the variations of the vessels diameters revealed a significant difference between the two populations. This difference indicates a probable association between glaucoma and variations of the vessel diameter.

In this paper, an algorithm for automatic point correspondence is proposed towards retinal image registration. Given a pair of corresponding retinal images and a set of bifurcations or other salient points in one of the images, the algorithm detects effectively the set of corresponding points in the second image, by exploiting the properties of Kohonen's Self Organizing Maps and embedding them in a stochastic optimization procedure. The proposed algorithm was tested on 20 unimodal retinal pairs and the obtained results show an enhanced performance in terms of accuracy and robustness compared to the existing algorithm, on which it is based.

In this paper, an automatic method for registering multimodal retinal images is presented. The method consists of three steps: the vessel centerline detection and extraction of bifurcation points only in the reference image, the automatic correspondence of bifurcation points in the two images using a novel implementation of the Self Organized Maps (SOMs) and the extraction of the parameters of the affine transform using the previously obtained correspondences. The proposed registration algorithm was tested on 24 multimodal retinal pairs and the obtained results show an advantageous performance in terms of accuracy with respect to the manual registration.