Major Axis Length Research Articles

Improvement of classification performance of Parkinson's disease using shape features for machine learning on dopamine transporter single photon emission computed tomography.

To assess the classification performance between Parkinson's disease (PD) and normal control (NC) when semi-quantitative indicators and shape features obtained on dopamine transporter (DAT) single photon emission computed tomography (SPECT) are combined as a feature of machine learning (ML). A total of 100 cases of both PD and normal control (NC) from the Parkinson's Progression Markers Initiative database were evaluated. A summed image was generated and regions of interests were set to the left and right striata. Area, equivalent diameter, major axis length, minor axis length, perimeter and circularity were calculated as shape features. Striatum binding ratios (SBRputamen and SBRcaudate) were used as comparison features. The classification performance of the PD and NC groups according to receiver operating characteristic analysis of the shape features was compared in terms of SBRs. Furthermore, we compared the classification performance of ML when shape features or SBRs were used alone and in combination. The shape features (except minor axis length) and SBRs indicated significant differences between the NC and PD groups (p < 0.05). The top five areas under the curves (AUC) were as follows: circularity (0.972), SBRputamen (0.972), major axis length (0.945), SBRcaudate (0.928) and perimeter (0.896). When classification was done using ML, AUC was as follows: circularity and SBRs (0.995), circularity alone (0.990), and SBRs (0.973). The classification performance was significantly improved by combining SBRs and circularity than by SBRs alone (p = 0.018). We found that the circularity obtained from DAT-SPECT images could help in distinguishing NC and PD. Furthermore, the classification performance of ML was significantly improved using circularity in SBRs together.

Lymphangiogenesis in Hodgkin Lymphoma and in Indirectly Related Conditions – A Review

The significance of lymphangiogenesis in malignant tumors and specifically in classic Hodgkin lymphoma has not been properly evaluated, in contrast with that of angiogenesis in these neoplasms. In the study reviewed herein, the relevance of lymphatic vessel proliferation was explored in 19 cases of classic Hodgkin lymphoma stained with the D2-40 (anti-podoplanin) antibody. In each case, three lymphatic vessel hot spots were analyzed twice. Of the 57 hot spots thus investigated, 15 were chosen at random for image acquisition and analysis and microvessel counting. The mean perimeter, major axis length, surface area and complexity factor for each hot spot were established by morphometry, and associations with clinical and laboratory traits of classic Hodgkin lymphoma were identified. No relationships were found with the clinical features or immunostaining for typical markers of this lymphoma. However, significant inverse relations were found with BAX, pRb and IκB-α expression in tumor cells, genes believed to play roles in apoptosis in this lymphoma. The mean lymphatic major axis length was inversely correlated with the complexity factor. Clinicopathological associations were further obtained for the expression of BAX, pRb, and IκB-α in a large cohort of classic Hodgkin lymphoma patients that was previously published. Since the issue was poorly explored at this point, we have reviewed the significance of lymphangiogenesis in indirectly associated conditions.

Assessment of body balance of patients treated with the Ilizarov method for tibial nonunion

The biomechanics of the musculoskeletal system in patients after tibial nonunion treatment using the Ilizarov method have not yet been fully explored. From the orthopaedic and patient point of view, after the treatment, an assessment should be carried out of the biomechanics of the musculoskeletal system. The aim of this study was to assess the body balance of patients treated with the Ilizarov method for tibial nonunion. The research group included 24 individuals with a mean age of 55 years, who were treated for aseptic tibial nonunion with the Ilizarov method. The control group was matched to the study group in terms of gender and age, and consisted of 32 subjects with a mean age of 50.5 years and no significant medical history. This study evaluated the balance of patients with the use of pedobarography. In the control group, a statistically significantly shorter path of centre of gravity was observed. There were no statistical differences between the study and control groups for the field area of the centre of gravity. There were no statistical differences between the study and control groups for the minor axis length or major axis length of the centre of gravity. There was a relationship between the centre of pressure path length and the age of the participants in both the control group and the study group. Treatment of patients with tibial nonunion with the Ilizarov fixator achieves similar balance to healthy volunteers. In the pedobarographic evaluation, patients treated for tibial nonunion using the Ilizarov method had similar statics of the musculoskeletal system to healthy volunteers.

Computer Vision-based Human Activity Recognition for Elderly Home Care

Video-based monitoring of elderly people at home receives more attention in recent days. In this paper, we propose a novel approach to develop smart monitoring system for elderly people using computer vision techniques. Gaussian Mixture Model (GMM) based algorithm is used for background and foreground separation inorder to track the activities of human object. The minimum bounding box of the human object is traced and features like major axis length, minor axis length and orientation angle are extracted. The proposed approach is evaluated on the video sequences of fall dataset.

Rice Grain Classification using Multi-class Support Vector Machine (SVM)

&lt;span&gt;Presently, the demands for rice are increasing. This will affects the need for producing and sorting rice grain in faster and exceed the normal requirement. However, the manual rice classification using naked eyes are not very accurate and only professionals are able to do it. Machine learning is found to be a suitable technique for rice classification in producing an accurate result and faster solution. Thus, a study on the classification of rice grain using an image processing technique is presented. The rice grain image went through the pre-processing process which includes the grayscale and binary conversion, and segmentation before the feature extraction process. Four attributes of shape descriptor which are area, perimeter, major axis length, and minor axis length and three attributes of color descriptor which are hue, saturation and value were extracted from each rice grain image. In another note, a Multi-class Support Vector Machine (SVM) is used to classify the three types of rice grain which are basmathi, ponni and brown rice. The performance of the proposed study is evaluated to 90 testing images which returned 92.22% of classification accuracy. The study is expected to assist the Agrotechnology industry in automatic classification of rice grain in the future.&lt;/span&gt;

Rupture Prediction of Abdominal Aortic Aneurysm using Matlab, LabVIEW and Python in CT images

Abdominal Aortic Aneurysm (AAA) is a substantial enlargement of the aorta that weakens the blood vessel wall leads to atherosclerotic disease. Aneurysms are dangerous because when it rupture causing hemorrhage. This leads to insufficient blood flow from the aorta to the different organs of the human body. Based on the extracted features from the AAA and the demographic data of the patient, the clinical management team will decide the optimal treatment procedure. The screening of AAA is done by ultrasound scan to know the type of the aneurysm present. Diameter with 05.05 cm or more, it should be viewed seriously. The AAA images were segmented using connected components labeling technique. Early detection of aneurysm can be repaired with proper treatment so that it does not rupture. The CT images of AAA were read and denoisedto preserve the edges using image processing software. Based on the histogram of the filtered image the AAA area was identified and threshold the image to view clearly the dilated part. The area, major axis length, minor axis length of the AAA images were calculated to understand the flow dynamics and to predict the rupture of the AAA wall using MATLAB, LabVIEW and python program.

Morfología de semillas de siete especies del género Opuntia (Cactaceae) del sureste de Coahuila (México)

Introducción y Objetivos: Opuntia se distribuye naturalmente desde Canadá hasta Argentina, actualmente forma parte del paisaje en algunas regiones del mundo, comprende entre 191 y 215 especies; la conservación ex situ de este recurso fitogenético ha adquirido relevancia, pero la identificación de especies por semilla es una limitante cuando no se tienen caracteres morfológicos confiables. El objetivo del estudio fue caracterizar la morfología de semillas de siete especies de Opuntia e identificar caracteres morfológicos propios, para tener un criterio en la identificación de especies cuando el material vegetal conservado es semilla. M&amp;M: Mediante la técnica de desgaste y análisis de imágenes se evaluó: grosor de testa (GT), grosor de la cubierta funicular ventral (GCFV), longitud del embrión (LE), área del embrión (AE), longitud del eje mayor (LEMA) y del eje menor (LEME). Por medio de ANDEVA exploratorios y componentes principales (CP) se detectaron los caracteres más sobresalientes. Para la clasificación y predicción de especies se realizó un análisis supervisado con los métodos K-NN y LDA.&#x0D; Resultados: Las diferencias en todas las variables entre especies fueron significativas. Opuntia ficus-indica presentó semillas de mayor tamaño. Las variables que más ayudan a discriminar son GCFV y GT. El análisis de componentes principales explicó el 92% de la variación total. El método de predicción K-NN, fue capaz de predecir correctamente el 83% de los casos en la clasificación de las especies.&#x0D; Conclusiones: Los caracteres de semillas evaluados, principalmente el GCFV y GT, pueden coadyuvar en la descripción morfológica de las especies de nopal o o en identificación de especies de Opuntia cuando sólo se tiene semilla.

High-resolution simulations of catastrophic disruptions: Resultant shape distributions

The members of asteroid families have various shapes. We investigate the origin of their shapes by high-resolution impact simulations for catastrophic disruptions using a Smoothed Particle Hydrodynamics code. Collisional remnants produced through our simulations of the catastrophic disruptions mainly have spherical or bilobed shapes. However, no flat remnants with the ratio of minor to major axis lengths c/a≲0.5 are formed. The results of the simulations provide various shapes of asteroids and explain most of the shapes in asteroid families that are supposed to be produced through catastrophic disruptions. However, the present simulations do not explain significantly flat asteroids. We suggest that these flat asteroids may be interlopers or formed through low-velocity collisions between member asteroids.

Automatic Tajweed Rules Recognition using K-Nearest Neighbour (K-NN)

Tajweed refers to a pronunciation rule for Al-Quran recitation in Islam. It acts as guidance for Muslims in reciting the Al-Quran in a correct manner. Yet, Tajweed rules could be complicated as it consists of various types of laws. It could also be confusing, and difficult to remember particularly for the people who have less knowledge in Tajweed rules. Thus, a study on automatic tajweed rules recognition using image processing technique is proposed. The scope of this study is limited to Idgham laws only. Initially, the input image went through the pre-processing process which includes four sub-processes which are greyscale conversion, binary conversion, thinning and flip, and word segmentation. Next, six attributes of shape descriptor which are major axis length, minor axis length, eccentricity, filled area, solidity, and perimeter were extracted from each input image. A technique of k-Nearest Neighbour (k-NN) is employed to recognize the two types of Idgham Laws which are Idgham Maal Ghunnah and Idgham Bila Ghunnah. The performance of the proposed study is evaluated to 180 testing images which returned 84.44% of classification accuracy. The outcome of this study is expected to recognize the Tajweed rules automatically and may assist the user on a proper recitation of Al-Quran.

The Effect of Drifter GPS Errors on Estimates of Submesoscale Vorticity

AbstractDifferential kinematic flow properties (DKP), such as vertical vorticity, have been estimated from surface drifters. However, previous DKP error estimates were a posteriori and did not include correlated errors across drifters. To accurately estimate submesoscale (≤1 km) DKPs from drifters, errors must be better understood. Here, the a priori vorticity standard error is derived that depends upon the number of drifters in the cluster, the drifter cluster major and minor axes lengths, the instrument velocity error, and the cross-drifter error correlation. Two stationary GPS experiments, with zero vorticity, were performed at separations of O(101–103) m to understand vorticity error and test the derivation using 1 Hz position differences and Doppler shift velocities. Vorticity errors of ±5f (where f is the local Coriolis parameter)were found for ≈40 m separations. The frequency-dependent velocity variances and GPS-to-GPS correlations are quantified. Vorticity estimated with a “blended” velocity has reduced error. The stationary vorticity error can be well predicted given velocity error, correlation, and minor axis length. Vorticity error analysis is applied to submesoscale-sampling in situ GPS drifters near Point Sal, California. The derivation predicts when large high-frequency vorticity fluctuations (indicating noise) occur. Previously, cluster area or ellipticity were used as criteria to distinguish error. We show that the drifter cluster minor axis (narrowness) is a key time-dependent factor affecting vorticity error, and even for velocity errors &lt;0.004 m s−1, the vorticity error exceeds ±5f when cluster minor axis &lt;50 m. These results will aid submesoscale drifter deployment planning.

Postural Control and Plantar Pressure Symmetry in Male and Female Athletes With Chronic Low Back Pain When Performing Overhead Squat

Background: Low Back Pain (LBP) is one of the most prevalent problems associated with sport activities. The present study aimed to investigate the relationship between pain and plantar pressure variables. In addition, we compared these variables in male and female athletes with LBP. Methods: In this study, 47 participants with the age range of 18 to 25 years were selected (22 males and 25 females). Visual Analogue Scale (VAS) was used to evaluate the LBP severity. The plantar pressure variables were recorded using a plantar pressure measurement device (model: Foot pressing FDM-S) made by Zebris Company. Results: Length of minor axis (p male= 0.020, p female= 0.227), length of major axis (p male= 0.041, p female= 0.011), area of sway (p male= 0.0001, p female= 0.007), path length (p male= 0.053, p female= 0.001), velocity of sway (p male= 0.023, p female= 0.008), and standard deviation X (p male= 0.048, p female= 0.147) of the COP variables had a positive and significant relationship with the pain. The symmetry of plantar pressure variables did not show strong correlation with the pain intensity (p>0.05). The COP variables also showed a significant difference in area of sway (p=0.042), path length (p=0.044), and standard deviation X (p=0.043) between the males and females. Females had more oscillations than males, but there was no difference in the symmetry of plantar pressure variables between the males and females (p>0.05). Conclusion: LBP is a factor that can impair the postural control, resulting in increased risk of injury among the athletes with LBP, especially in females.

3D characterization of defects in deep-powder-bed manufactured Ti–6Al–4V and their influence on tensile properties

Deep-powder-bed additive manufacturing (AM) can lead to distinctive microstructural features. In this study, 300-mm long cylindrical rods (12-mm diameter) of Ti–6Al–4V were vertically built to the limit height of a commercial selective electron beam melting (SEBM) system for quantitative three-dimensional (3D) characterization of the defects by X-ray micro-computed tomography (μ-CT). Detailed μ-CT data from 18,337 consecutive slices revealed a strong dependence of defect characteristics on build height, including defect volume, population, sphericity, major axis length, depth and orientation angle. The first 100-mm build exhibited the worst presence of defects by each measurement, while the middle and last 100-mm builds contained much fewer defects, especially the last 100-mm build, which was free of lack-of-fusion defects (sphericity < 0.5). As a result, the first 100-mm build displayed 50% lower reduction of area and 20% lower strain-to-fracture than the last 100-mm build, while the tensile strengths varied within just ±3%. An outer 3-mm thick ring and a central 1.5-mm diameter region were found to contain substantially less defects along the 300-mm build height. The dependence of defect features on build height was attributed to the existence of an upward temperature gradient during SEBM. The 3D defect features revealed by μ-CT along the build height provide important implications for deep-powder-bed AM by SEBM.

Modes of an elliptical cylindrical resonant cavity—analytical solution

An analytical solution of the Helmholtz equation for electromagnetic field distribution in a resonant cavity with elliptic cross section is found. We compare the frequencies of the eigenmodes with numerical and experimental values for a metallic cavity and find an excellent matching. We focus our analysis on the microwave frequency region and show how the ellipticity of the cavity (ratio of the minor and major axes length b/a) influences several mode frequencies and also the Q-factor of the cavity. By doing so, we demonstrate how the elliptic geometry splits the degeneracy of certain modes of the circular cylindric cavity.

Nuclear Deformation in Response to Mechanical Confinement is Cell Type Dependent.

Mechanosensing of the mechanical microenvironment by cells regulates cell phenotype and function. The nucleus is critical in mechanosensing, as it transmits external forces from the cellular microenvironment to the nuclear envelope housing chromatin. This study aims to elucidate how mechanical confinement affects nuclear deformation within several cell types, and to determine the role of cytoskeletal elements in controlling nuclear deformation. Human cancer cells (MDA-MB-231), human mesenchymal stem cells (MSCs), and mouse fibroblasts (L929) were seeded within polydimethylsiloxane (PDMS) microfluidic devices containing microchannels of varying cross-sectional areas, and nuclear morphology and volume were quantified via image processing of fluorescent cell nuclei. We found that the nuclear major axis length remained fairly constant with increasing confinement in MSCs and MDA-MB-231 cells, but increased with increasing confinement in L929 cells. Nuclear volume of L929 cells and MSCs decreased in the most confining channels. However, L929 nuclei were much more isotropic in unconfined channels than MSC nuclei. When microtubule polymerization or myosin II contractility was inhibited, nuclear deformation was altered only in MSCs in wide channels. This work informs our understanding of nuclear mechanics in physiologically relevant spaces, and suggests diverging roles of the cytoskeleton in regulating nuclear deformation in different cell types.

Validation of the ImageJ package for alpha track counting on Solid State Nuclear Track Detectors

In the present work, an alpha spectroscopic method using CR39 nuclear track detectors is being evaluated, elaborating on track parameters from different alpha particle sources. The freely-available, Java-based ImageJ software was used to obtain the major and minor axis length, the area and the mean gray level of the recorded tracks. A multi-paramater approach based on Principal Component Analysis of the data was subsequently applied and succeeded in grouping the recorded tracks according to alpha-particle energy. The methodology was further applied for the separation of radon progeny on CR39 detectors exposed in a radon chamber.

Combined Multimodal Analysis of Peripheral Retinal and Macular Circulation in Diabetic Retinopathy (COPRA Study)

To systematically examine the relationships between the microvascular indices that are measured on OCT angiography (OCTA) and the presence and extent of peripheral nonperfusion in persons with diabetic retinopathy. A retrospective cross-sectional study of patients who had varying degrees of diabetic retinopathy. The study sample was recruited from 2 large tertiary referral retina clinics. In total, 82 eyes of 45 patients with varying degrees of diabetic retinopathy were enrolled and analyzed. Relationships between peripheral ischemia measured on fluorescein angiography (FA) and OCTA metrics, including foveal avascular zone (FAZ) and vessel density measurements. A significant decrease in mean signal index in both the superficial and deep plexus and binarized flow index in the superficial plexus were found with increasing duration of diabetes mellitus. OCT and OCTA grading showed increasing central macular thickness and prevalence of microvascular abnormalities in the superficial and deep capillary bed with worse retinopathy as measured on the Diabetic Retinopathy Severity Scale. FAZ area and major axis and minor axis length were strongly associated with diabetic retinopathy severity. On classifying eyes into tertiles of peripheral ischemia measured on FA, significant increases in various FAZ metrics, including FAZ area and minor axis length, were noted. Statistically worsening of FAZ OCTA metrics was only seen between tertiles 2 and 3, indicating a non-linear relationship. The presence of neovascularization of the disc, neovascularization elsewhere, or intraretinal microvascular abnormality was associated with a significant increase in FAZ major axis length in the superficial plexus and a significant decrease in binarized flow index in the deep plexus. OCTA metrics are indicators of the severity of peripheral retinal nonperfusion. However, the central ischemic index did not exhibit a linear relationship with peripheral capillary nonperfusion. Our findings suggest that a rise in intraocular vascular endothelial growth factor as a consequence of mild peripheral capillary nonperfusion may play a compensatory role in maintaining the central macular microcirculation. Further investigations with studies employing longitudinal design will improve our understanding of the relationship between macular microcirculation and peripheral ischemia.

Collisional elongation: Possible origin of extremely elongated shape of 1I/‘Oumuamua

Light curve observations of a recently discovered interstellar object 1I/‘Oumuamua suggest that this object has an extremely elongated shape with the axis ratio 0.3 or smaller. Planetesimal collisions can produce irregular shapes including elongated shapes. In this paper, we suggest that the extremely elongated shape of 1I/‘Oumuamua may be the result of such an impact. To find detailed impact conditions to form the extremely elongated objects, we conduct numerical simulations of planetesimal collisions using Smoothed Particle Hydrodynamics method for elastic dynamics with self-gravity and interparticle friction. Impacts into strengthless target planetesimals with radius 50 m are conducted with various ratios of impactor mass to target mass q, friction angles ϕd, impact velocities vimp, and impact angles θimp. We find that impacts with q ≥ 0.5, ϕd ≥ 40°, vimp ≤ 40 cm/s, and θimp ≤ 30° produce remnants with the ratio of intermediate to major axis length <0.3. This impact condition suggests that the parent protoplanetary disk in the planetesimal collision stage was weakly turbulent (α < 10−4 for the inner disk) and composed of planetesimals smaller than ∼7 km to ensure small impact velocity.

Difficulty of predicting lymph node metastasis on CT in patients with rectal neuroendocrine tumors.

BackgroundSurgical indications for rectal neuroendocrine tumors with potential lymph node metastasis remain controversial. Although accurate preoperative diagnosis of nodal status may be helpful for treatment strategy, scant data about clinical values of lymph node size have been reported. The aim of this retrospective study was to investigate the relationship between lymph node size and lymph node metastasis.MethodsParticipants comprised 102 patients who underwent rectal resection with total mesenteric excision or tumor-specific mesenteric excision and in some cases additional lateral pelvic lymph node dissection for rectal neuroendocrine tumor between June 2005 and September 2016. All lymph nodes from specimens were checked and measured.ResultsPathological lymph node metastasis was confirmed in 37 patients (36%), including 6 patients (5.8%) with lateral pelvic lymph node metastasis. A total of 1169 lymph nodes in the mesorectum were retrieved from all specimens, with 78 lymph nodes (6.7%) showing metastasis. Mean length (long-axis diameter) of metastatic lymph nodes in the mesorectum was 4.31 mm, significantly larger than that of non-metastatic lymph nodes (2.39 mm, P<0.01). The optimal cut-off of major axis length for predicting mesorectal lymph node metastasis was 3 mm. We could predict lymph node metastasis in only 7 patients (21%) from preoperative multidetector-row computed tomography.ConclusionsMetastatic lymph nodes were small, so predicting lymph node metastasis from preoperative computed tomography is difficult. Alternative modalities with a scan width less than 3 mm may be needed to predict lymph node metastasis of rectal NET with low cost and labour requirements.

Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer.

While important in diagnosis of breast cancer, the scientific assessment of the role of imaging in prognosis of outcomes and treatment planning is limited. To evaluate the potential of using quantitative imaging variables for stratifying risk of distant recurrence in breast cancer patients. Retrospective. In all, 892 female invasive breast cancer patients. Dynamic contrast-enhanced MRI with field strength 1.5 T and 3 T. Computer vision algorithms were applied to extract a comprehensive set of 529 imaging features quantifying size, shape, enhancement patterns, and heterogeneity of the tumors and the surrounding tissue. Using a development set with 446 cases, we selected 20 imaging features with high prognostic value. We evaluated the imaging features using an independent test set with 446 cases. The principal statistical measure was a concordance index between individual imaging features and patient distant recurrence-free survival (DRFS). The strongest association with DRFS that persisted after controlling for known prognostic clinical and pathology variables was found for signal enhancement ratio (SER) partial tumor volume (concordance index [C] = 0.768, 95% confidence interval [CI]: 0.679-0.856), tumor major axis length (C = 0.742, 95% CI: 0.650-0.834), kurtosis of the SER map within tumor (C = 0.640, 95% CI: 0.521-0.760), tumor cluster shade (C = 0.313, 95% CI: 0.216-0.410), and washin rate information measure of correlation (C = 0.702, 95% CI: 0.601-0.803). Quantitative assessment of breast cancer features seen in a routine breast MRI might be able to be used for assessment of risk of distant recurrence. 4 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2019.

Finger Recognition by Feature Length

A small circle cake in a finger image is used to extract the partial finger image. Subsequently, the major axis length of this extracted circle cake is computed. Next, more circle cakes are extracted and the major axis length of each individual circle cake is computed. These obtained major axis lengths are chained together circularly to generate recognition features. These features are used to recognize a finger. The chain-generated feature is compared to its adjacent features to locate the most match one. For every pixel in the sample image, the address of the match target in the unknown image is recorded. The trajectories from the sample image to unknown image can be similar and can be diverted significantly depending on the similarity of the two compared images. If the trajectories are similar, the match targets can hold together. Otherwise, the match targets are deviated to different places. The convergence and deviation situation can be used as an indictor to show whether the two compared images are coming from the identical source or not.