A Proposed Device for Controlling the Flow of Information Based on Weyl Fermions.

In this work we propose a novel device for controlling the flow of information using Weyl fermions. Based on a previous work by our group, we show that it is possible to fully control the flow of Weyl fermions on several different channels by applying an electric field perpendicular to the direction of motion of the particles on each channel. In this way, we can transmit information as logical bits, depending on the existence or not of a Weyl current on each channel. We also show that the response time of this device is exceptionally low, less than 1 ps, for typical values of its parameters, allowing for the control of the flow of information at extremely high rates of the order of 100 Petabits per second. Alternatively, this device could also operate as an electric field sensor. In addition, we demonstrate that Weyl fermions can be efficiently guided through the proposed device using appropriate magnetic fields. Finally, we discuss some particularly interesting remarks regarding the electromagnetic interactions of high-energy particles.

Validation of a web-based distance visual acuity test.

PURPOSE: To validate the Democritus Digital Acuity and Reading Test (DDART) as a distance VA (dVA) test against a series of prevalent conventional distance vision charts. SETTING: Ophthalmology Department, University Hospital of Alexandroupolis, Alexandroupolis, Greece; Ophthalmology Department, AHEPA University Hospital, Thessaloniki, Greece; and Ophthalmica Institute of Ophthalmology & Microsurgery, Thessaloniki, Greece. DESIGN: Prospective multicenter validation study. METHODS: The distance best spectacle-corrected visual acuity (dBSCVA) was compared in normal (NVG) and low (LVG) vision participants against 4 prevalent conventional distance vision charts (ETDRS, Snellen, Landolt C, and Tumbling E) by a predefined 2.5-symbol noninferiority margin and intraclass correlation coefficients (ICCs). DDART's test-retest (TRT) reliability was assessed with ICCs. RESULTS: 534 participants (471 and 63 with normal and low vision, respectively) were included in the study. The mean difference between dBSCVA measured with DDART and conventional charts ranged between -0.84 and +0.85 symbols, without exceeding the 2.5-symbol noninferiority margin. ICCs indicated an excellent level of agreement for all patient groups (from 0.848 to 0.985). TRT reliability indicated differences below 1 symbol both for the NVG and LVG, with ICCs ranging between 0.912 and 0.964 for the 4 DDARTs. CONCLUSIONS: DDART was a valid web-based dVA test that provided reliable measurements in clinical and telemedical settings, both for normal and low vision patients.

Development and Validation of the First Smart TV-Based Visual Acuity Test: A Prospective Study.

(1) Background: While smartphones are among the primary devices used in telemedical applications, smart TV healthcare apps are not prevalent despite smart TVs' penetrance in home settings. The present study's objective was to develop and validate the first smart TV-based visual acuity (VA) test (Democritus Digital Visual Acuity Test (DDiVAT)) that allows a reliable VA self-assessment. (2) Methods: This is a prospective validation study. DDiVAT introduces several advanced features for reliable VA self-testing; among them: automatic calibration, voice recognition, voice guidance, automatic calculation of VA indexes, and a smart TV-based messaging system. Normal and low vision participants were included in the validation. DDiVAT VA results (VADDiVAT) were compared against the ones from: (a) the gold-standard conventional ETDRS (VAETDRS), and, (b) an independent ophthalmologist who monitored the self-examination testing (VARES). Comparisons were performed by noninferiority test (set at 2.5-letters) and intraclass correlation coefficients (ICCs). DDiVAT's test-retest reliability was assessed within a 15-day time-window. (3) Results: A total of 300 participants (185 and 115 with normal and low vision, respectively) responded to ETDRS and DDiVAT. Mean difference in letters was -0.05 for VAETDRS-VARES, 0.62 for VARES-VADDiVAT, and 0.67 for VAETDRS-VADDiVAT, significantly lower than the 2.5 letter noninferiority margin. ICCs indicated an excellent level of agreement, collectively and for each group (0.922-0.996). All displayed letters in DDiVAT presented a similar difficulty. The overall accuracy of the voice recognition service was 96.01%. ICC for VADDiVAT test-retest was 0.957. (4) Conclusions: The proposed DDiVAT presented non-significant VA differences with the ETDRS, suggesting that it can be used for accurate VA self-assessment in telemedical settings, both for normal and low-vision patients.

Image Quality Comparison between Digital Breast Tomosynthesis Images and 2D Mammographic Images Using the CDMAM Test Object.

To evaluate the image quality (IQ) of synthesized two-dimensional (s2D) and tomographic layer (TL) mammographic images in comparison to the 2D digital mammographic images produced with a new digital breast tomosynthesis (DBT) system. Methods: The CDMAM test object was used for IQ evaluation of actual 2D images, s2D and TL images, acquired using all available acquisition modes. Evaluation was performed automatically using the commercial software that accompanied CDMAM. Results: The IQ scores of the TLs with the in-focus CDMAM were comparable, although usually inferior to those of 2D images acquired with the same acquisition mode, and better than the respective s2D images. The IQ results of TLs satisfied the EUREF limits applicable to 2D images, whereas for s2D images this was not the case. The use of high-dose mode (H-mode), instead of normal-dose mode (N-mode), increased the image quality of both TL and s2D images, especially when the standard mode (ST) was used. Although the high-resolution (HR) mode produced TL images of similar or better image quality compared to ST mode, HR s2D images were clearly inferior to ST s2D images. Conclusions: s2D images present inferior image quality compared to 2D and TL images. The HR mode produces TL images and s2D images with half the pixel size and requires a 25% increase in average glandular dose (AGD). Despite that, IQ evaluation results with CDMAM are in favor of HR resolution mode only for TL images and mainly for smaller-sized details.

Machine learning and features for the prediction of thermal sensation and comfort using data from field surveys in Cyprus.

Perception can influence individuals' behaviour and attitude affecting responses and compliance to precautionary measures. This study aims to investigate the performance of methods for thermal sensation and comfort prediction. Four machine learning algorithms (MLA), artificial neural networks, random forest (RF), support vector machines, and linear discriminant analysis were examined and compared to the physiologically equivalent temperature (PET). Data were collected in field surveys conducted in outdoor sites in Cyprus. The seven- and nine-point assessment scales of thermal sensation and a two-point scale of thermal comfort were considered. The models of MLA included meteorological and physiological features. The results indicate RF as the best MLA applied to the data. All MLA outperformed PET. For thermal sensation, the lowest prediction error (1.32 points) and the highest accuracy (30%) were found in the seven-point scale for the feature vector consisting of air temperature, relative humidity, wind speed, grey globe temperature, clothing insulation, activity, age, sex, and body mass index. The accuracy increased to 63.8% when considering prediction with at most one-point difference from the correct thermal sensation category. The best performed feature vector for thermal sensation also produced one of the best models for thermal comfort yielding an accuracy of 71% and an F-score of 0.81.

Impact of personality on the decision process and on satisfaction rates in pseudophakic presbyopic correction.

PURPOSE: To explore the impact of personality on the decision process and satisfaction rates in pseudophakic presbyopic correction. SETTING: Department of Ophthalmology, University Hospital of Alexandroupolis, Greece. DESIGN: Prospective, comparative study. METHODS: A consistent consultation was conducted in patients with cataract that explained the benefits and the drawbacks of bilateral trifocal correction, which was offered at no extra cost. In all participants, personality was evaluated by The Traits Personality Questionnaire 5. Data modeling with decision trees and multiple regression analysis identified the contributions of personality traits to the decision process and postoperative satisfaction. RESULTS: Of 120 participants (60 men and 60 women), 81 (67.5%, 24 men, 57 women) selected premium correction. In men, low neuroticism and high extraversion were the primary personality contributors for selecting premium surgery. In women, all personality traits contributed to the selection process. Women were more demanding in the expected postoperative distant acuity than men (0.1 vs 0.2 logMAR) to present high satisfaction. For both men and women, openness to experience, conscientiousness, and extraversion are primary contributors for optimal satisfaction rates. CONCLUSIONS: Men and women demonstrate differences in the selection process for premium pseudophakic surgery and differences in the expected postoperative visual acuity. It seems that the personality of the patient plays a significant role in the perceived outcome after premium surgery.

Premium Monovision versus Bilateral Myopic Monovision, Hybrid Monovision and Bilateral Trifocal Implantation: A Comparative Study.

Purpose: Contemporary monovision techniques use premium intraocular lenses (IOLs), either in both eyes or at least in the non-dominant one. Primary objective of this study was to compare the efficacy of premium monovision (implantation of the trifocal diffractive Panoptix IOL in the non-dominant eye and the bifocal hybrid refractive-diffractive Restor IOL in the dominant eye), against bilateral myopic monovision (implantation of the monofocal SN60WF IOL targeting -0.50 D in the dominant eye and -1.25 D myopia in the non-dominant one), hybrid monovision (implantation of Panoptix in the non-dominant eye and SN60WF in the dominant eye) and bilateral trifocal implantation (with bilateral Panoptix implantation). Methods: This is a prospective, comparative, clinic-based trial. Cataract patients populated four study groups: Monovision Group (MoG), Multifocal Lens Group (MfG), Hybrid Monovision Group (HmG) and Premium Monovision Group (PmG). Binocular Uncorrected Distance Visual Acuity (UDVA), Uncorrected Reading Acuity and Critical Print Size at 60cm (UIRA, UICPS) and at 40cm (UNRA, UNCPS), contrast sensitivity, vision-related functional impairment, dysphotopsia symptoms and spectacle dependence were evaluated 6 months following the operation of the second eye. A mathematical model was constructed, which calculated the relative efficacy of each surgical intervention. Results: A total of 120 participants were recruited and populated equally the study groups. Significant improvement of preoperative UDVA was observed in all study groups. No significant differences could be detected in postoperative UDVA and UIRA (p = 0.24) among study groups, while significant differences were noticed in UICPS (p = 0.04), UNRA (p = 0.02) and UNCPS (p = 0.01). Dysphotopic phenomena (glare and shadows) were significantly more in the MfG arm followed by the PmG group (p = 0.04 and p = 0.02, respectively), while perceived difficulty and spectacle independence rates were significantly better in PmG group. PmG presented the best overall relative efficacy. Conclusion: All surgical techniques present satisfactory outcomes. Premium monovision seems to demonstrate the best outcomes. Trial Registration: ClinicalTrials.gov, NCT04618380. Registered 05 November 2020, https://clinicaltrials.gov/ct2/show/NCT04618380.

Video-Based Eye Blink Identification and Classification.

Blink detection and classification can provide a very useful clinical indicator, because of its relation with many neurological and ophthalmological conditions. In this work, we propose a system that automatically detects and classifies blinks as "complete" or "incomplete" in high resolution image sequences zoomed into the participants' face, acquired during clinical examination using near-Infrared illumination. This method utilizes state-of-the-art (DeepLabv3+) deep learning encoder-decoder neural architecture -DLED to segment iris and eyelid in both eyes in the acquired images. The sequence of the segmented frames is post-processed to calculate the distance between the eyelids of each eye (palpebral fissure height) and the corresponding iris diameter. These quantities are temporally filtered and their fraction is subject to adaptive thresholding to identify blinks and determine their type, independently for each eye. The proposed system was tested on 15 participants, each with one video of 4 to 10 minutes. Several metrics of blink detection and classification accuracy were calculated against the ground truth, which was generated by three (3) independent experts, whose conflicts were resolved by a senior expert. Results show that the proposed system achieved F1-score 95.3% and 80.9% for the classification of complete and incomplete blinks respectively, collectively for all 15 participants, outperforming all 3 experts. The proposed system was proven robust in handling unexpected participant movements and actions, as well as glare and reflections from the spectacles, or face obstruction by facemasks.

Lighting Standards Revisited: Introduction of a Mathematical Model for the Assessment of the Impact of Illuminance on Visual Acuity.

PURPOSE: Primary objective of present study is to introduce a contemporary methodology for the lighting standards update addressing both normophakic and pseudophakic patients. METHODS: For the sake of our study, we theoretically estimated the intraocular-to-crystalline lens iIluminance ratio (ICIR) and the intraocular lens (IOL) luminous efficiency function VIOL(λ) as a new lighting benefit metric. Then, in a sample of 24 pseudophakic patients (38 eyes) implanted with the trifocal diffractive IOL Panoptix (SG) and in a control group (CG) of 28 normophakic participants (50 eyes), uncorrected distance visual acuity (UDVA) was measured at illuminance of 550lx (optimal UDVA). Following dark adaptation, illuminance was gradually raised from 20 lx until illuminance level that the patient reached his/her optimal UDVA. This measured illuminance at this point was defined as the minimum required illuminance level (MRIL). MRIL and UDVA for illuminance levels between 20 and 550lx in SG were compared with the corresponding values in CG. MRIL calculation allowed the construction of a predictive mathematical model that estimates the impact of environmental lighting on UDVA. RESULTS: ICIR for Panoptix eyes ranged from 54.00% to 55.99%. Both groups had significantly higher UDVA at 550lx compared to 20lx (p < 0.05). CG had significantly higher UDVA than SG at 20lx (7.20 letters, p = 0.045), while no significant difference was detected at 550lx (0.40 letters, p = 0.883). SG required significantly more illuminance than CG to maintain their UDVA (MRILSG= 191.05lx, MRILCG= 122lx, p = 0.007). Our predictive model suggests suboptimal UDVA in a series of lighting directives for normophakic and Panoptix eyes. CONCLUSION: This is the first study to introduce the VIOL(λ) as a new lighting benefit metric and a mathematical model that quantifies the impact of illuminance on UDVA in normophakic and pseudophakic patients. CLINICALTRIALSGOV IDENTIFIER: NCT04263636.

Development and Validation of a Web-Based Reading Test for Normal and Low Vision Patients.

PURPOSE: To develop and validate a web-based reading test for normal and low vision patients. METHODS: This is a prospective, comparative trial. The web-based Democritus Digital Acuity Reading Test (wDDART) was developed. wDDART introduces a series of advanced characteristics (advanced text calibration, computer-vision-based estimation of patient's distance, and automatic calculation of patient's reading times) that facilitate the overall examination procedure. wDDART's reading parameters [reading acuity (RA), maximum reading speed (MRS), critical print size (CPS) and reading accessibility index (ACC)] were compared to the corresponding ones of its conventional Windows-based reading test (DDART) in a sample of normal and low vision participants. wDDART's test-retest reliability for all reading parameters was evaluated in a 15-day time-window. RESULTS: One hundred patients (normal vision group-NVG: 70; low vision group-LVG: 30 patients) responded to DDART and wDDART. Non-significant differences between the two reading tests were found for all parameters in NVG and LVG. Intraclass correlation coefficients (ICCs) between the two tests demonstrated good or excellent correlation for RA, MRS, ACC and moderate correlation for CPS. Test-retest reliability was excellent for RA and ACC, while ICCs were 0.715-0.895 for MRS and CPS. CONCLUSION: The wDDART demonstrated sufficient validity and repeatability making it suitable for clinical and research settings. CLINICALTRIALSGOV IDENTIFIER: NCT04618224.

Introduction of a digital near-vision reading test for normal and low vision adults: development and validation.

BACKGROUND: MNREAD is an advanced near-vision acuity chart that has already been translated and validated in Greek language. Considering that no validated Greek digital near-vision test exists, our primary objective was to develop and validate a digital near-vision reading test based on the fundamental properties of the Greek printed MNREAD (MNREAD-GR). METHODS: This is a prospective, comparative study. A digital near-vision chart was developed (Democritus Digital Acuity Reading Test - DDART) with text size calibration, audio recording for automatic reading timing, as well as automatic calculation of reading acuity (RA), maximum reading speed (MRS), critical print size (CPS) and reading accessibility index (ACC). Normal and low vision subjects participated in the validation process, responding to MNREAD-GR and DDART at the same day, at a 40 cm viewing distance. Differences in all parameters between the charts were compared with t-test and intraclass correlation coefficients (ICCs). Within 15 days, all participants responded again to DDART in a different set of sentences to assess its test-retest reliability. RESULTS: One hundred patients (normal vision group - NVG: 70 patients; low vision group - LVG: 30 patients) responded to both reading tests. Non-significant differences were detected for all parameters between DDART and MNREAD-GR except for MRS and ACC that were significantly higher in MNREAD-GR in NVG (p <  0.01). NVG participants demonstrated sufficient ICCs that ranged from 0.854 to 0.963, while LVG demonstrated ICCs for RA, ACC, MRS and CPS equal to 0.986, 0.894, 0.794 and 0.723, respectively. All parameters calculated with DDART demonstrated excellent test-retest reliability (ICCs: 0.903 - 0.956). CONCLUSIONS: The proposed reading test presented comparable validity and repeatability to MNREAD-GR suggesting that it can be used both in normal and low vision Greek patients. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04242836. Registered 24 January 2020 - Retrospectively registered.

Measurement and Modeling of Microbial Growth Using Timelapse Video.

The development of timelapse videos for the investigation of growing microbial colonies has gained increasing interest due to its low cost and complexity implementation. In the present study, a simple experimental setup is proposed for periodic snapshot acquisition of a petri dish cultivating a fungus of the genus Candida SPP, thus creating a timelapse video. A computational algorithm, based on image processing techniques is proposed for estimating the microbial population and for extracting the experimental population curves, showing the time evolution of the population of microbes at any region of the dish. Likewise, a novel mathematical population evolution modeling approach is reported, which is based on the logistic function (LF). Parameter estimation of the aforementioned model is described and visually assessed, in comparison with the conventional and widely-used LF method. The effect of the image analysis parameterization is also highlighted. Our experiments take into account different area sizes, i.e., the number of pixels in the neighborhood, to generate population curves and calculate the model parameters. Our results reveal that, as the size of the area increases, the curve becomes smoother, the signal-to-noise-ratio increases and the estimation of model parameters becomes more accurate.

An Automated Method for Quality Control in MRI Systems: Methods and Considerations.

OBJECTIVE: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. MATERIALS AND METHODS: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. RESULTS: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. CONCLUSIONS: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.

Enhancing classification accuracy utilizing globules and dots features in digital dermoscopy.

The interest in image dermoscopy has been significantly increased recently and skin lesion images are nowadays routinely acquired for a number of skin disorders. An important finding in the assessment of a skin lesion severity is the existence of dark dots and globules, which are hard to locate and count using existing image software tools. In this work we present a novel methodology for detecting/segmenting and count dark dots and globules from dermoscopy images. Segmentation is performed using a multi-resolution approach based on inverse non-linear diffusion. Subsequently, a number of features are extracted from the segmented dots/globules and their diagnostic value in automatic classification of dermoscopy images of skin lesions into melanoma and non-malignant nevus is evaluated. The proposed algorithm is applied to a number of images with skin lesions with known histo-pathology. Results show that the proposed algorithm is very effective in automatically segmenting dark dots and globules. Furthermore, it was found that the features extracted from the segmented dots/globules can enhance the performance of classification algorithms that discriminate between malignant and benign skin lesions, when they are combined with other region-based descriptors.

A new formula for bivariate Hermite interpolation on variable step grids and its application to image interpolation.

In this paper, we present a novel formula of the bivariate Hermite interpolating (BHI) polynomial in the case of support points arranged on a grid with variable step. This expression is applicable when interpolation of a bivariate function is required, given its value and the values of its partial derivatives of arbitrarily high order, at the support points. The proposed formula is a generalization of an existing formula for the bivariate Hermite polynomial. It is also algebraically much simpler, thus can be computed more efficiently. In order to apply Hermite interpolation to image interpolation, we simplify the proposed (BHI) to handle support points on a regular unit-step grid. The values of image partial derivatives are arithmetically approximated using compact finite differences. The proposed method is being assessed in a number of image interpolation experiments that include a synthetic image, for which the values of the partial derivatives are computed analytically, as well as a collection of images from different medical modalities. The proposed BHI with up to second-order image partial derivatives, outperforms the convolution-based interpolation methods, as well as generalized interpolation methods with the same number of support points that was compared with, in the majority of image interpolation experiments. The computational load of the proposed BHI is calculated and its behaviour with respect to its controlling parameters is investigated.

An implicit evolution scheme for active contours and surfaces based on IIR filtering.

In this work, we present an approach for implementing an implicit scheme for the numerical solution of the partial differential equation of the evolution of an active contour/surface. The proposed scheme is applicable to any variant of the traditional active contour (AC), irrespectively of the calculation of the image-based force field and it is readily applicable to explicitly parameterized active surfaces (AS). The proposed approach is formulated as an infinite impulse response (IIR) filtering of the coordinates of the contour/surface points. The poles of the filter are determined by the parameters controlling the shape of the active contour/surface. We show that the proposed IIR-based implicit evolution scheme has very low complexity. Furthermore, the proposed scheme is numerically stable, thus it allows the convergence of the AC/AS with significantly fewer iterations than the explicit evolution scheme. It also possesses the separability property along the two parameters of the AS, thus it may be applied to deformable surfaces, without the need to store and invert large sparse matrices. We implemented the proposed IIR-based implicit evolution scheme in the Vector Field Convolution (VFC) AC/AS using synthetic and clinical volumetric data. We compared the segmentation results with those of the explicit AC/AS evolution, in terms of accuracy and efficiency. Results show that the VFC AC/AS with the proposed IIR-based implicit evolution scheme achieves the same segmentation results with the explicit scheme, with considerably less computation time.

A classification system based on a new wrapper feature selection algorithm for the diagnosis of primary and secondary polycythemia.

Primary and Secondary Polycythemia are diseases of the bone marrow that affect the blood's composition and prohibit patients from becoming blood donors. Since these diseases may become fatal, their early diagnosis is important. In this paper, a classification system for the diagnosis of Primary and Secondary Polycythemia is proposed. The proposed system classifies input data into three classes; Healthy, Primary Polycythemic (PP) and Secondary Polycythemic (SP) and is implemented using two separate binary classification levels. The first level performs the Healthy/non-Healthy classification and the second level the PP/SP classification. To this end, a novel wrapper feature selection algorithm, called the LM-FM algorithm, is presented in order to maximize the classifier's performance. The algorithm is comprised of two stages that are applied sequentially: the Local Maximization (LM) stage and the Floating Maximization (FM) stage. The LM stage finds the best possible subset of a fixed predefined size, which is then used as an input for the next stage. The FM stage uses a floating size technique to search for an even better solution by varying the initially provided subset size. Then, the Support Vector Machine (SVM) classifier is used for the discrimination of the data at each classification level. The proposed classification system is compared with various well-established feature selection techniques such as the Sequential Floating Forward Selection (SFFS) and the Maximum Output Information (MOI) wrapper schemes, and with standalone classification techniques such as the Multilayer Perceptron (MLP) and SVM classifier. The proposed LM-FM feature selection algorithm combined with the SVM classifier increases the overall performance of the classification system, scoring up to 98.9% overall accuracy at the first classification level and up to 96.6% at the second classification level. Moreover, it provides excellent robustness regardless of the size of the input feature subset used.

A novel tool for segmenting 3D medical images based on generalized cylinders and active surfaces.

Three-dimensional (3D) medical imaging has been incorporated in routine clinical practice, since the required infrastructure has become increasingly affordable. New algorithms and applications are needed to serve the additional image processing and analysis functions in 3D space. In this work we propose a system for semi-automatic modeling and segmentation of elongated salient and anatomical objects in 3D medical images. The proposed methodology is based on a novel mathematical formalization of a well-known class of geometric primitives, namely generalized cylinders (GCs), which exhibits advantages over the existing parametric definition. Since the anatomical objects have to be modeled by their intersection with the transverse image planes, the proposed methodology includes also a new seeded region growing (SRG) segmentation algorithm for ellipse detection in 2D images, based on a priori shape knowledge. Finally, the resulting GC model is used to initialize an active surface (AS) segmentation method, in order to accurately delineate the required object. In this work we present the proposed algorithms in detail, along with the evaluation of the accuracy of the model-based segmentation by experts. Results show that elongated objects like the aorta and the trachea may be segmented with sensitivity between 90% and 95%. The proposed SRG-ellipse detector requires minimal user-initialization and its executions requires only few seconds for each image slice on an average laptop. The evolution of the AS requires less than one second per iteration for a typical CT image. Comparisons are provided with state of the art semi-automatic medical image processing software, which validate the merit of the proposed work.

Hermite kernels for slice interpolation in medical images.

Univariate Hermite interpolation of the total degree (HTD) is an algebraically demanding interpolation method that utilizes information of the values of the signal to be interpolated at distinct support positions, as well as the values of its derivatives up to a maximum available order. In this work the interpolation kernels of the univariate HTD are derived, using several approximations of the 1st and 2nd order of discrete signal derivative. We assess the derived Hermite kernels in the task of medical image slice interpolation, against several other well established interpolation techniques. Results show that specific Hermite kernels can outperform other established interpolation methods with similar computational complexity, in terms of root mean square error (RMSE), in a number of interpolation experiments, resulting in higher accuracy interpolated images.

Automatic point correspondence using an artificial immune system optimization technique for medical image registration.

In this paper, an automatic method for determining pairs of corresponding points between medical images is proposed. The method is based on the implementation of an artificial immune system (AIS). AIS is a relatively novel, population based category of algorithms, inspired by theoretical immunologic models. When used as function optimizers, AIS have the attractive property of locating the global optimum of a function as well as a large number of strong local optimum points. In this work, AIS has been applied both for the extraction of an optimal set of candidate points on the reference image and the definition of their corresponding ones on the second image. The performance of the proposed AIS algorithm is evaluated against the widely used Iterative Closest Point (ICP) algorithm in terms of the accuracy of the obtained correspondences and in terms of the accuracy of the point-based registration by the two correspondence algorithms and the Mutual Information criterion, as an intensity-based registration method. Qualitative and quantitative results involving 92 X-ray dental and 10 retinal image pairs subject to known and unknown transformations are presented. The results indicate a superior performance of the proposed AIS algorithm with respect to the ICP algorithm and the Mutual Information, in terms of both correct correspondence and registration accuracy.