Difference In Percentage Area Research Articles

Syndesmosis Evaluation Under WBCT: 3rd Base to Get a Cut-Off Point to Diagnosis

Category: Sports; Arthroscopy Introduction/Purpose: Evaluation of syndesmal injury remains challenging for Foot & Ankle Surgeons. As objective data to provide an accurate preoperative diagnosis of syndesmal instability is lacking, arthroscopy has remained the Gold Standard. However, the costs and risks associated with surgery make arthroscopy challenging to indicate in all cases. Therefore, significant interest exists in determining objective criteria to safely indicate surgery. Comparative WBCT is a valuable tool, allowing a precise evaluation of the healthy and injured joints. Specifically, the difference in the syndesmal area correlates with diastasis at the inferior tibiofibular joint, while the cut value for diagnostic instability remains undetermined. Objective: To compare the difference in syndesmal area between healthy patients and patients with syndesmal sprains under WBCT. Methods: IRB approval was obtained for a retrospective case-control study. Patients with suspected syndesmal injuries from April 2019 to August 2022 were included in the case group. Patients with chronic ankle instability, age over 55 years, or surgery of the inferior limb were excluded. The control group was obtained from a previous study of comparative WBCT of the ankle joint, obtained for reasons different than trauma or instability. All patients were evaluated under WBCT by a fellowship-trained MSK radiologist. After evaluating the syndesmal area at 1 cm from the ankle joint line in an axial WBCT slice, the percentage difference from the injured to the healthy side was recorded. Demographic data were obtained from the patient chart. For those patients undergoing surgical stabilization, the operative note was examined. Statistical evaluation was performed by an independent statistician using SPSSv27 using the ANOVA test. Results: Thirty-nine patients were recruited for the study group, and a control group of 25 patients with healthy ankles was obtained. Demographics data for both groups were comparable Arthroscopy was indicated in 7/39 patients (17,9%), which confirmed diagnosis and warranted stabilization of the joint. The mean percentage area difference of the confirmed injured syndesmosis was 15,14% (IC 4,89-25,39), suspected injured syndesmosis was 9,25% (IC 3,11-15,39), and the healthy syndesmosis was 1,54 (IC 1,08-2,01). Statistically significant differences were obtained for healthy, operated, and non-operated patients with a p=0,021. Conclusion: A comparative WBCT scan and the percentage difference in the syndesmal area between the healthy, confirmed, and suspected syndesmal injury demonstrated a handy diagnostic tool. Although the limitations of this study, our results suggest that a 15% difference in the syndesmal area between healthy and injured sides might be used as the cut-off point to indicate diagnostic arthroscopy.

Abstract 14690: Deep Learning Assisted Target Lesion Analysis Using Advanced Border Detection Algorithm of Intravascular Ultrasound

The vessel-plaque interface detection and lesion length of coronary artery disease are still challenging using the deep learning with intravascular ultrasound (IVUS). We used AI-based image segmentation technology to develop a deep learning model for classifying frames with IVUS-derived target lesion. Methods. A total of 3,782 target lesion frames from 28 acute coronary syndrome(ACS) patients were randomized into training and test sets in a 2:1 ratio. The training algorithm was based on the measurement of the core lab. A segmentation model using non-local blocks was used to increase the similarity between pixels of the same class in a U-Net-based model. The edge detection was performed on the region of interest, and the detected edge was used as an aid in deriving the final segmentation result. And the morphological guided measurements were used to provide the information about the vessel area, lumen area, plaque burden and lesion length. Quantitative evaluation was conducted on four scales: Jaccard Measure (JM), Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and Percentage of Area Difference (PAD). And we compared the diagnostic accuracy to detect the vessel-plaque interface of the target lesion. Results. In the training sample (1,256 IVUS frames of target lesion), and the test samples (1,072 IVUS frames of target lesion) were used for the proposed model. The external elastic membrane area (EEM), lumen area, plaque area and thrombus were assessed with the proposed algorithm. The measurement of EEM was DSC 0.99, JM 0.98, HD 0.06, and PAD 0.01. And the lumen also shows excellent performance with DSC 0.98, JM 0.97, HD 0.08, and PAD 0.01. In addition, the proposed model was compared with ground-truth for Thrombus, and showed high values of 0.88 for DSC, 0.83 for JM, 0.21 for HD, and 0.09 for PAD. Conclusion. An advanced algorithm can accurately detect the vessel-plaque interface and estimated stent length of coronary artery disease with high reproducibility. F

Effect of Different Nursing Interventions on Discharged Patients with Cardiac Valve Replacement Evaluated by Deep Learning Algorithm-Based MRI Information.

This study was aimed to explore the application of cardiac magnetic resonance imaging (MRI) image segmentation model based on U-Net in the diagnosis of a valvular heart disease. The effect of continuous nursing on the survival of discharged patients with cardiac valve replacement was analyzed in this study. In this study, the filling completion operation, cross entropy loss function, and guidance unit were introduced and optimized based on the U-Net network. The heart MRI image segmentation model ML-Net was established. We compared the Dice, Hausdorff distance (HD), and percentage of area difference (PAD) values between ML-Net and other algorithms. The MRI image features of 82 patients with valvular heart disease who underwent cardiac valve replacement were analyzed. According to different nursing methods, they were randomly divided into the control group (routine nursing) and the intervention group (continuous nursing), with 41 cases in each group. The Glasgow Outcome Scale (GOS) score and the Self-rating Anxiety Scale (SAS) were compared between the two groups to assess the degree of anxiety of patients and the survival status at 6 months, 1 year, 2 years, and 3 years after discharge. The results showed that the Dice coefficient, HD, and PAD of the ML-Net algorithm were (0.896 ± 0.071), (5.66 ± 0.45) mm, and (15.34 ± 1.22) %, respectively. The Dice, HD, and PAD values of the ML-Net algorithm were all statistically different from those of the convolutional neural networks (CNN), fully convolutional networks (FCN), SegNet, and U-Net algorithms (P < 0.05). Atrial, ventricular, and aortic abnormalities can be seen in MRI images of patients with valvular heart disease. The cardiac blood flow signal will also be abnormal. The GOS score of the intervention group was significantly higher than that of the control group (P < 0.01). The SAS score was lower than that of the control group (P < 0.05). The survival rates of patients with valvular heart disease at 6 months, 1 year, 2 years, and 3 years after discharge were significantly higher than those in the control group (P < 0.05). The abovementioned results showed that an effective segmentation model for cardiac MRI images was established in this study. Continuous nursing played an important role in the postoperative recovery of discharged patients after cardiac valve replacement. This study provided a reference value for the diagnosis and prognosis of valvular heart disease.

Efficacy of Rotary Retreatment Techniques Assisted with Passive Ultrasonic Activation of Resin Solvent in Removal of Gutta-percha with Epoxy Resin and MTA Based Root Canal Sealers: An In-vitro Study

Introduction: Endodontic retreatment is indicated in cases of failed primary endodontic treatment. The main goal of retreatment is to establish conditions that will enable the healing of the periapical tissue. Aim: To evaluate the efficacy of rotary retreatment with the ProTaper Universal Retreatment system supplemented with passive ultrasonic activation of solvent in retreatment of singlerooted mandibular premolar teeth obturated with gutta-percha and two different root canal sealers (AH Plus and MTA Fillapex). Materials and Methods: This in-vitro study was conducted in the Department of Conservative Dentistry and Endodontics at Mahatma Gandhi Mission’s Dental College and Hospital, Navi Mumbai, Maharashtra, India, from January 2019 to April 2019. Total 66 freshly extracted human single rooted mandibular premolars were used in this study. Root canal preparation was done using ProTaper Next rotary files. Teeth were decoronated to get a uniform length of 18 mm and were divided into three retreatment groups based on the technique used for retreatment. Group I included rotary retreatment, Group II involved rotary retreatment with solvent and paper point wicking, and Group III involved rotary retreatment with passive ultrasonic activation of solvent and paper point wicking. Based on the sealer used for obturation, teeth in each group were further divided into two subgroups, subgroup A with AH Plus sealer and subgroup B with MTA Fillapex sealer. Teeth were stored in an incubator for 30 days to allow sealer to set after which sectioning of the specimens was done buccolingually for examination under the stereomicroscope and photographed. Statistical analysis was done by using parametric Analysis of Variance (ANOVA) followed by Post-hoc tests and Unpaired t-test. Results: No statistically significant difference in percentage area of residual root canal filling material noted between the group I and II and when obturated with gutta-percha and AH Plus sealer (subgroup A) as p-value=0.215. Whereas, it revealed a statistically significant difference between the group I, II, and III when obturated with gutta-percha and MTA Fillapex sealer (subgroup B) as p-value&lt;0.001. Conclusion: Passive ultrasonic activation of solvent after guttapercha removal using ProTaper Universal rotary files could improve the quality of endodontic retreatment.

Convolutional Neural Network Intelligent Segmentation Algorithm-Based Magnetic Resonance Imaging in Diagnosis of Nasopharyngeal Carcinoma Foci.

The aim of this study was to explore the adoption value of convolutional neural network- (CNN-) based magnetic resonance imaging (MRI) image intelligent segmentation model in the identification of nasopharyngeal carcinoma (NPC) lesions. The multisequence cross convolutional (MSCC) method was used in the complex convolutional network algorithm to establish the intelligent segmentation model two-dimensional (2D) ResUNet for the MRI image of the NPC lesion. Moreover, a multisequence multidimensional fusion segmentation model (MSCC-MDF) was further established. With 45 patients with NPC as the research objects, the Dice coefficient, Hausdorff distance (HD), and percentage of area difference (PAD) were calculated to evaluate the segmentation effect of MRI lesions. The results showed that the 2D-ResUNet model processed by MSCC had the largest Dice coefficient of 0.792 ± 0.045 for segmenting the tumor lesions of NPC, and it also had the smallest HD and PAD, which were 5.94 ± 0.41 mm and 15.96 ± 1.232%, respectively. When batch size = 5, the convergence curve was relatively gentle, and the convergence speed was the best. The largest Dice coefficient of MSCC-MDF model segmenting NPC tumor lesions was 0.896 ± 0.09, and its HD and PAD were the smallest, which were 5.07 ± 0.54 mm and 14.41 ± 1.33%, respectively. Its Dice coefficient was lower than other algorithms (P < 0.05), but HD and PAD were significantly higher than other algorithms (P < 0.05). To sum up, the MSCC-MDF model significantly improved the segmentation performance of MRI lesions in NPC patients, which provided a reference for the diagnosis of NPC.

Extraction of media adventitia and luminal intima borders by reconstructing intravascular ultrasound image sequences with vascular structural continuity.

Most published methods directly achieve vessel membrane border detection on cross-sectional intravascular ultrasound (IVUS) images. The vascular structural continuity that exists in entire IVUS image sequences has been overlooked. However, this continuity can have a helpful role in the delineation of vessel membrane contours. To achieve the vessel membrane segmentation more effectively through employing this continuity, a strategy, referred to as multiangle reconstruction, segmentation, and recovery (RSR), is proposed in this paper. Four main steps are contained in the multiangle-RSR: first, a combination of sampling and interpolation is employed to reconstruct long-axis-model IVUS frames, in which continuity information becomes available. Second, a clustering algorithm is conducted on long-axis-model IVUS frames to roughly extract the media-adventitia (MA) and lumen-intima (LI) boundaries. Third, the segmentation results of cross-sectional IVUS frames are recovered based on the rough results of long-axis-model IVUS frames, and an optimization process that combines downsampling, fitting and smoothing is designed to reduce the interference of bifurcation and side vessels. Multiangle-RSR is tested on a public dataset, and the Hausdorff distance (HD), Jaccard measure (JM), and percentage of area difference (PAD) are utilized as quantitative evaluation metrics. Mean HDs of 0.34 and 0.29mm are obtained for MA border detection and LI border detection, respectively, which decrease by 43.3% and 9.4%, respectively, compared with their counterparts in previously published approaches. Furthermore, the mean JM is 0.87 for both MA border detection and LI border detection. The mean PADs of the MA contour extraction and the LI contour extraction are 0.10 and 0.11, respectively. The results indicate that the proposed strategy effectively introduces vascular structural continuity by reconstructing long-axis-model IVUS frames and achieves more precise extraction of MA and LI borders.

Current signal processing-based methods to discriminate internal faults from magnetizing inrush current

Two new methods, current change ratio (CCR) and percentage area difference (PAD) were proposed to solve a problem of how to distinguish between internal faults and inrush condition when transformer is switched on. This problem may delay operation or may mal-operate some protection schemes like deferential protection. The methods were concluded after observing and analyzing the behavior and shape of large number of both inrush and internal fault signals that had been obtained using a model transformer in a laboratory. The methods were practically tested on a three-phase transformer with rated power of 20 kVA at Cardiff University’s laboratory and the data were processed using LabVIEW and MATLAB programs. The results showed that internal faults can be correctly distinguished from inrush condition within a short time (from 5 to 10 ms), particularly the minor internal faults such as the interturn fault which is submerged to inrush current and make it is too difficult to be detected. The advantages of these algorithms are simple in design and faster than the second harmonic method which is the most popular method used for solving this problem.

Performance Assessment of K-Means and KNN Algorithm for the Detection of Borders and Extraction of Plaque Area from IVUS Images

The coronary artery vascular disease of atherosclerosis, which made the blood vessel artery wall harden and narrow. The vascular wall disease quantitatively analyzed and diagnosed by an intravascular ultrasound (IVUS) image. The quantitative investigations of coronary atherosclerosis by means of IVUS and manual recognition of wall and plaque borders are restricted by the need for observers with considerable understanding and the tedious environment of manual border detection. To improve and provide more detailed vessel and plaque information for better diagnosis and assessment go for an automated segmentation. An automated construction for the purpose of perceiving lumen and media-adventitia borders in IVUS images was effectively formulated. An effectual unsupervised K-Means clustering scheme refine the borders with morphological operations, later an IVUS data samples area classified using supervised KNN(K-Nearest Neighbor) classifier to extract the plaque feature. The performance of contour metric measurements in terms of Jaccard Index (JI), percentage area difference (PAD), area error (AE), dice index (DI), false positive ratio (RFP), in addition to false negative ratio (RFN) are computed for evaluation and variation.

Vessel membrane segmentation and calcification location in intravascular ultrasound images using a region detector and an effective selection strategy

Background and objectiveSegmenting vessel membranes and locating the calcific region in intravascular ultrasound (IVUS) images aid physicians in the diagnosis of atherosclerosis. However, the manual extraction of the media adventitia (MA)/lumen border and calcification location are cumbersome due to the excessive number of IVUS frames. Moreover, most existing (semi-)automatic detection methods cannot achieve both vessel membrane extraction and calcification location simultaneously, and they are unable to detect vessel membranes in IVUS frames from different acquisition systems. MethodA fully automatic approach is proposed based on extremal regions and a flexible selection strategy to extract vessel membranes in different IVUS frames and locate the calcific region in high-frequency ones. Three main steps are included in the algorithm. First, a region detector is employed to extract extremal regions from an IVUS image. Then, according to the selection strategy, a part of the extracted regions is selected. At the same time, the calcification is located according to its special acoustic properties. Next, approximate MA and lumen border segmentation is achieved based on the selected extremal regions and the located calcification in polar coordinates. Finally, the final segmentation results are obtained by smoothing the approximate values. ResultTo demonstrate the feasibility of the method, it was evaluated based on a standard public dataset. Furthermore, to quantitatively evaluate the segmentation performance, the Hausdorff distance (HD), Jaccard measure (JM) and percentage of area difference (PAD) were used. The results show that a mean HD of 1.13/1.21 mm, a mean JM of 0.83/0.77 and a mean PAD of 0.11/0.23 are achieved for MA/lumen border detection in 77 40-MHz IVUS images. For MA/lumen border extraction in 435 20-MHz IVUS frames, the average HD, JM and PAD values are 0.47/0.28 mm, 0.84/0.89 and 0.13/0.10, respectively. In addition, the approach successfully achieves calcification location in 40-MHz IVUS frames. In comparison with other published methods, the method proposed in this study is competitive. ConclusionAccording to these results, our strategy can extract MA/lumen borders in different IVUS frames and effectively locate calcification in high-frequency IVUS frames.

IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution

Reliable detection of the media-adventitia border (MAB) and the lumen-intima border (LIB) in intravascular ultrasound (IVUS) images remains a challenging task that is of high clinical interest. In this paper, we propose a superpixel-wise fuzzy clustering technique modified by edges, followed by level set evolution (SFCME-LSE), for automatic border extraction in 40 MHz IVUS images. The contributions are three-fold. First, the usage of superpixels suppresses the influence of speckle noise in ultrasound images on the clustering results. Second, we propose a region of interest (ROI) assignment scheme to prevent the segmentation from being distracted by pathological structures and artifacts. Finally, the contour is converged towards the target boundary through LSE with an appropriately improved edge indicator. Quantitative evaluations on two IVUS datasets by the Jaccard measure (JM), the percentage of area difference (PAD), and the Hausdorff distance (HD) demonstrate the effectiveness of the proposed SFCME-LSE method. SFCME-LSE achieves the minimal HD of 1.20 ± 0.66 mm and 1.18 ± 0.70 mm for the MAB and LIB, respectively, among several state-of-the-art methods on a publicly available dataset.

Lumen boundary detection using neutrosophic c-means in IVOCT images

In this paper, a novel method for lumen boundary identification is proposed using Neutrosophic c_means. This method clusters pixels of the intravascular optical coherence tomography image into several clusters using indeterminacy and Neutrosophic theory, which aims to detect the boundaries. Intravascular optical coherence tomography images are cross-sectional and high-resolution images which are taken from the coronary arterial wall. Coronary Artery Disease cause a lot of death each year. The first step for diagnosing this kind of diseases is to detect lumen boundary. Employing this approach, we obtained 0.972, 0.019, 0.076 mm2, 0.32 mm, and 0.985 as mean value for Jaccard measure (JACC), the percentage of area difference (PAD), average distance (AD), Hausdorff distance (HD), and dice index (DI), respectively. Based on our results, this method enjoys high accuracy performance.

IVUS images segmentation using spatial fuzzy clustering and hierarchical level set evolution

The detection of the lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images is crucial for quantifying plaque burdens. The challenge of the segmentation work mainly roots in various artifacts in the image. Most of the published methods involve the establishment of complex models but do not behave well on images with artifacts. In this study, aiming at automatically delineating borders in IVUS frames acquired by 20 MHz ultrasound probes, we present a fuzzy clustering-initialized hierarchical level set evolution (FC-HLSE) method. A cluster selection strategy based on the spatial fuzzy c-means (FCM) is proposed to generate the initial value and regularization term of the level set evolution (LSE). The contour convergence splits into two LSE steps between which an ingenious contour extraction (consisting of the morphological processing, the seek and linear interpolation, the gradient-based and circular fitting-based refinement) is carried out. We evaluate the proposed methodology on the publicly available 435 images by comparing auto-segmented results with the ground truth. The performance of the method is quantified using the Jaccard measure (JM), the Hausdorff distance (HD), the percentage of area difference (PAD), the linear regression and Bland-Altman analysis. Results reveal that our method can handle images with or without artifacts. The algorithm is able to extract the lumen/MA border with the JM of 0.90/0.89, the HD of 0.31/0.40 mm, the PAD of 0.07/0.08 in average, which is better in some cases compared with several state-of-the-art methods.

Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images

Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature.

Automatic segmentation of the lumen region in intravascular images of the coronary artery

Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen. An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two experts in 326 IVUS images of the coronary artery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference and Dice coefficient were 0.88 ± 0.06, 0.29 ± 0.17 mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324 IVUS images successfully segmented. Additionally, a comparison with the studies found in the literature showed that the proposed method is slight better than the majority of the related methods that have been proposed. Hence, the new automatic segmentation method is shown to be effective in detecting the lumen in IVUS images without using complex solutions and user interaction.

HoliMAb: A holistic approach for Media–Adventitia border detection in intravascular ultrasound

We present a fully automatic methodology for the detection of the Media-Adventitia border (MAb) in human coronary artery in Intravascular Ultrasound (IVUS) images. A robust border detection is achieved by means of a holistic interpretation of the detection problem where the target object, i.e. the media layer, is considered as part of the whole vessel in the image and all the relationships between tissues are learnt. A fairly general framework exploiting multi-class tissue characterization as well as contextual information on the morphology and the appearance of the tissues is presented. The methodology is (i) validated through an exhaustive comparison with both Inter-observer variability on two challenging databases and (ii) compared with state-of-the-art methods for the detection of the MAb in IVUS. The obtained averaged values for the mean radial distance and the percentage of area difference are 0.211 mm and 10.1%, respectively. The applicability of the proposed methodology to clinical practice is also discussed.

Segmentation of Masses from Breast Ultrasound Images using Parametric Active Contour Algorithm

The active contour (Snake) is a computer generated curve that can trace boundaries of images. As a method which applies the computer technology in mathematics, Snake is computationally formulated based on controlled continuous splines and adopts the mathematical concept of energy minimization. This paper presents the application of Snake for the segmentation of masses on breast ultrasound images. The images used are taken from Malaysian population. The boundaries of the masses identified may be used in classification of cancers or non-cancerous masses. Specifically the Balloon Snake is applied in segmenting the masses in the breast ultrasound images. Comparison on the masses areas segmented by the Balloon Snake is done against the areas traced by an expert (radiologist). It is found that from forty-five masses tested, the average percentage area difference of Balloon Snake is 4.47%. This implies that the accuracy of segmentation results for the Balloon Snake is 95.53%.

ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure

This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For denosing, the SNR improvement criterion is used, while for compression, we have considered the compression ratio (CR), the percentage area difference (PAD), and the weighted diagnostic distortion (WDD) measure. Several Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH) ECG databases are used for performance evaluation. Simulation results illustrate that both applications can contribute to and enhance the clinical ECG data denoising and compression performance. For denoising, an average SNR improvement of 10.16 dB was achieved, which is 1.8 dB more than the next benchmark methods such as MABWT or EKF2. For compression, the algorithm was extended to include more than five Gaussian kernels. Results show a typical average CR of 11.37:1 with WDD << 1.73%. Consequently, the proposed framework is suitable for a hybrid system that integrates these algorithmic approaches for clean ECG data storage or transmission scenarios with high output SNRs, high CRs, and low distortions.

Quantitative evaluation of lip symmetry in skeletal asymmetry

The objective of this study was to investigate whether skeletal mandibular asymmetry associated with unilateral and anterior crossbite will lead to lip asymmetry. The subjects were 26 females, 13 controls and 13 true skeletal asymmetric age-matched patients (24-50 years). The study group was diagnosed as asymmetric according to visual and panoramic radiographic examination and exhibited a unilateral anterior crossbite, an asymmetric mandible, and a deviation of the chin. The control group was visually symmetric and exhibited all forms of tooth malalignment without the presence of a crossbite. Digitized images of the frontal facial photographs of all the subjects, taken in an intercuspal contact position, were analysed for upper and lower lip symmetry pre- and post-orthodontic treatment. The upper and lower lips were subdivided into two quadrants each and the surface area of each quadrant was measured and expressed as a percentage of the total surface area of the relevant lip. The degree of lip asymmetry was obtained by calculating the difference in percentage area between the two quadrants of each lip. In the study group, the lower lip quadrant on the crossbite side was increased pre-treatment (56.85 +/- 1.75 per cent), while the contralateral side was reduced (43.15 +/- 1.75 per cent, P < 0.005), resulting in 13.7 per cent asymmetry. After treatment, the respective lip areas were 52.12 +/- 0.64 and 47.88 +/- 0.64 (P < 0.01) and asymmetry was significantly reduced (4.25 +/- 1.29 per cent). In the control group, the differences between the lower lip quadrant areas (range 48.5-51.5 per cent) were small (less than 3 per cent asymmetry) and did not change post-treatment. The findings demonstrate that in this study group, lower lip symmetry was mainly controlled by the support provided through the dental interarch relationships and less by skeletal factors.

Visual and PCR assessment of light leaf spot (Pyrenopeziza brassicae) on winter oilseed rape (Brassica napus) cultivars

Methods to assess light leaf spot (Pyrenopeziza brassicae) on winter oilseed rape cultivars were compared in laboratory, controlled‐environment and field experiments. In controlled‐environment experiments with seedling leaves inoculated at GS 1,4, the greatest differences in percentage area affected by P. brassicae sporulation were observed with inoculum concentrations of 4 × 103 or 4 × 104 spores mL−1, rather than 4 × 102 or 4 × 105 spores mL−1, but older leaves had begun to senesce before assessment, particularly where they were severely affected by P. brassicae. In winter oilseed rape field experiments, a severe light leaf spot epidemic developed in 2002/03 (inoculated, September/October rainfall 127·2 mm) but not in 2003/04 (uninoculated, September/October rainfall 40·7 mm). In‐plot assessments discriminated between cultivars best in February/March in 2003 and June in 2004, but sometimes failed to detect plots with many infected plants (e.g. March/April 2004). Ranking of cultivar resistance differed between seedling experiments done under controlled‐environment conditions and field experiments. The sensitivity of detection of P. brassicae DNA extracted from culture was greater using the PCR primer pair PbITSF/PbITSR than using primers Pb1/Pb2. P. brassicae was detected by PCR (PbITS primers) in leaves from controlled‐environment experiments immediately and up to 14 days after inoculation, and in leaves sampled from field experiments 2 months before detection by visual assessment.

Spatial and Temporal Localization of FGF-2 and VEGF in Healing Tooth Extraction Sockets in a Rabbit Model

The purpose of this study was to test the hypothesis that spatial and temporal localization of growth factors FGF-2 and VEGF in a rabbit tooth extraction socket model correlate with the histologic events of healing. Twenty-four male New Zealand white rabbits divided into 8 groups of 3 were used in the study. Incisor teeth were extracted from both jaws and the healing extraction socket with surrounding jaw bone was harvested at 48 hours, 4 days, 1, 2, 4, 8, 12, and 16 weeks. Tissues were fixed, decalcified, and processed for hematoxylin-eosin and immunohistochemical staining. The sections were stained to detect FGF-2 and VEGF. The stained sections were then imaged and an automated computer program was used to detect the brown diaminobenzidine stain that represented the growth factors of interest. Data was obtained in the form of percentage area and intensity of stain and analyzed using the analysis of variance (ANOVA - Tukey Kramer and Scheffe's post-test). Spatial and temporal differences in localization of FGF-2 and VEGF were observed across all time frames in both jaws. Statistically significant differences in percentage area and intensity of brown diaminobenzidine stain were seen temporally between FGF-2 and VEGF (P < .05). The results of this study showed positive correlation of histologic events to spatial and temporal localization of FGF-2 and VEGF in a rabbit tooth extraction model.