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Gray Matter Volume Abnormalities in Depressive Patients With and Without Anxiety Disorders
2014
Medicine
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Comorbidity with anxiety disorder is a relatively common occurrence in major depressive disorder. However, the unique and shared neuroanatomical characteristics of depression and anxiety disorders have not been fully identified. The aim of this study was to identify gray matter abnormalities and their clinical correlates in depressive patients with and without anxiety disorders. We applied voxel-based morphometry and region-of-interest analyses of gray matter volume (GMV) in normal controls (NC
doi:10.1097/md.0000000000000345
pmid:25546687
pmcid:PMC4602623
fatcat:5ogpnrsxajgt7fkto54kglmcai
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... group, n ¼ 28), depressive patients without anxiety disorder (DP group, n ¼ 18), and depressive patients with anxiety disorder (DPA group, n ¼ 20). The correlations between regional GMV and clinical data were analyzed. The DP group showed decreased GMV in the left insula (INS) and left triangular part of the inferior frontal gyrus when compared to the NC group. The DPA group showed greater GMV in the midbrain, medial prefrontal cortex, and primary motor/somatosensory cortex when compared to the NC group. Moreover, the DPA group showed greater GMV than the DP group in the frontal, INS, and temporal lobes. Most gray matter anomalies were significantly correlated with depression severity or anxiety symptoms. These correlations were categorized into 4 trend models, of which 3 trend models (ie, Models I, II, and IV) revealed the direction of the correlation between regional GMV and depression severity to be the opposite of that between regional GMV and anxiety symptoms. Importantly, the left INS showed a trend Model I, which might be critically important for distinguishing depressive patients with and without anxiety disorder. Our findings of gray matter abnormalities, their correlations with clinical data, and the trend models showing opposite direction may reflect disorder-specific symptom characteristics and help explain the neurobiological differences between depression and anxiety disorder. (Medicine 93(29):e345) Abbreviations: AMYG = amygdala, ANG = angular gyrus, ANOVA = analysis of variance, CSF = cerebrospinal fluid, DMPFC = dorsomedial prefrontal cortex, DP = depressive patients without anxiety disorder, DPA = depressive patients with anxiety disorder, GAD = generalized anxiety disorder, GLM = General Linear Model, GMV = gray matter volume, HAMA = Hamilton Anxiety Scale, HAMD = Hamilton Depressive Rating Scale, HIP = hippocampus, IFG = inferior frontal gyrus, IFGtriang = triangular part of the inferior frontal gyrus, INS = insula, ITG = inferior temporal gyrus, LING = lingual gyrus, MDD = major depressive disorder, MRI = magnetic resonance imaging, MTG = middle temporal gyrus, NC = normal controls, PHG = parahippocampal gyrus, PoCG = postcentral gyrus, PreCG = precentral gyrus, REC = rectus, RGMV = regional gray matter volume, ROI = region of interest, ROL = rolandic operculum, SAS = Self-Rating Anxiety Scale, SDS = Zung's Self-Rating Depression Scale, SFGdor = dorsal part of superior frontal gyrus, TGMV = total gray matter volume, VBM = voxel-based morphometry. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0, where it is permissible to download, share and reproduce the work in any medium, provided it is properly cited. The work cannot be changed in any way or used commercially.
Speckle reducing bilateral filter for cattle follicle segmentation
2010
BMC Genomics
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Ultrasound imaging technology has wide applications in cattle reproduction and has been used to monitor individual follicles and determine the patterns of follicular development. However, the speckles in ultrasound images affect the post-processing, such as follicle segmentation and finally affect the measurement of the follicles. In order to reduce the effect of speckles, a bilateral filter is developed in this paper. Results: We develop a new bilateral filter for speckle reduction in
doi:10.1186/1471-2164-11-s2-s9
pmid:21047390
pmcid:PMC2975414
fatcat:jm75pekhyzcglhszmeb25eoeke
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... d images for follicle segmentation and measurement. Different from the previous bilateral filters, the proposed bilateral filter uses normalized difference in the computation of the Gaussian intensity difference. We also present the results of follicle segmentation after speckle reduction. Experimental results on both synthetic images and real ultrasound images demonstrate the effectiveness of the proposed filter. Conclusions: Compared with the previous bilateral filters, the proposed bilateral filter can reduce speckles in both high-intensity regions and low intensity regions in ultrasound images. The segmentation of the follicles in the speckle reduced images by the proposed method has higher performance than the segmentation in the original ultrasound image, and the images filtered by Gaussian filter, the conventional bilateral filter respectively.
Compression-Induced Tensile Mechanical Behaviors of the Crystalline Rock under Dynamic Loads
2020
Materials
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Characterization of the tensile mechanical behaviors of rocks under dynamic loads is of great significance for the practical engineering. However, thus far, its micromechanics have rarely been studied. This paper micromechanically investigated the compression-induced tensile mechanical behaviors of the crystalline rock using the grain-based model (GBM) by universal distinct element code (UDEC). Results showed that the crystalline rock has the rate- and heterogeneity-dependency of tensile
doi:10.3390/ma13225107
pmid:33198285
fatcat:gvdvpbb3hrbc7et2yg6gcbue2m
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... rs. Essentially, dynamic Brazilian tensile strength increased in a linear manner as the loading rate increased. With the size distribution and morphology of grain-scale heterogeneity weakened, it increased, and this trend was obviously enhanced as the loading rate increased. Additionally, the rate-dependent characteristic became strong with the grain heterogeneity weakened. The grain heterogeneity prominently affected the stress distribution inside the synthetic crystalline rock, especially in the mixed compression and tension zone. Due to heterogeneity, there were tensile stress concentrations (TSCs) in the sample which could favor microcracking and strength weakening of the sample. As the grain heterogeneity weakened or the loading rate increased, the magnitude of the TSC had a decreasing trend and there was a transition from the sharp TSC to the smooth tensile stress distribution zone. The progressive failure of the crystalline rock was notably influenced by the loading rate, which mainly represented the formation of the crushing zone adjacent to two loading points. Our results are meaningful for the practical engineering such as underground protection works from stress waves.
An improved approach for the segmentation of starch granules in microscopic images
2010
BMC Genomics
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Starches are the main storage polysaccharides in plants and are distributed widely throughout plants including seeds, roots, tubers, leaves, stems and so on. Currently, microscopic observation is one of the most important ways to investigate and analyze the structure of starches. The position, shape, and size of the starch granules are the main measurements for quantitative analysis. In order to obtain these measurements, segmentation of starch granules from the background is very important.
doi:10.1186/1471-2164-11-s2-s13
pmid:21047380
pmcid:PMC2975413
fatcat:n7lulf2fd5h3bc2upg2bvz42ke
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... ever, automatic segmentation of starch granules is still a challenging task because of the limitation of imaging condition and the complex scenarios of overlapping granules. Results: We propose a novel method to segment starch granules in microscopic images. In the proposed method, we first separate starch granules from background using automatic thresholding and then roughly segment the image using watershed algorithm. In order to reduce the oversegmentation in watershed algorithm, we use the roundness of each segment, and analyze the gradient vector field to find the critical points so as to identify oversegments. After oversegments are found, we extract the features, such as the position and intensity of the oversegments, and use fuzzy c-means clustering to merge the oversegments to the objects with similar features. Experimental results demonstrate that the proposed method can alleviate oversegmentation of watershed segmentation algorithm successfully. Conclusions: We present a new scheme for starch granules segmentation. The proposed scheme aims to alleviate the oversegmentation in watershed algorithm. We use the shape information and critical points of gradient vector flow (GVF) of starch granules to identify oversegments, and use fuzzy c-mean clustering based on prior knowledge to merge these oversegments to the objects. Experimental results on twenty microscopic starch images demonstrate the effectiveness of the proposed scheme.
High-efficiency method for recycling lithium from spent LiFePO4 cathode
2020
Nanotechnology Reviews
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The extraction of Li from the spent LiFePO4 cathode is enhanced by the selective removal using interactions between HCl and NaClO to dissolve the Li+ ion while Fe and P are retained in the structure. Several parameters, including the effects of dosage and drop acceleration of HCl and NaClO, reaction time, reaction temperature, and solid–liquid ratio on lithium leaching, were tested. The Total yields of lithium can achieve 97% after extraction process that lithium is extracted from the
doi:10.1515/ntrev-2020-0119
fatcat:d6tlxgaq25etbnsvyyu7r6jple
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... ed mother liquor, using an appropriate extraction agent that is a mixture of P507 and TBP and NF. The method also significantly reduced the use of acid and alkali, and the economic benefit of recycling is improved. Changes in composition, morphology, and structure of the material in the dissolution process are characterized by inductively coupled plasma optical emission spectrometry, scanning electron microscope, X-ray diffraction, particle size distribution instrument, and moisture analysis.
An Analytical Solution for Block Toppling Failure of Rock Slopes during an Earthquake
2017
Applied Sciences
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Toppling failure is one of the most common failure types in the field. It always occurs in rock masses containing a group of dominant discontinuities dipping into the slope. Post-earthquake investigation has shown that many toppling rock slope failures have occurred during earthquakes. In this study, an analytical solution is presented on the basis of limit equilibrium analysis. The acceleration of seismic load as well as joint persistence within the block base, were considered in the analysis.
doi:10.3390/app7101008
fatcat:i7t2sr5jjvdf7fon5lil3bnqqi
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... The method was then applied into a shake table test of an anti-dip layered slope model. As predicted from the analytical method, blocks topple or slide from slope crest to toe progressively and the factor of safety decreases as the inputting acceleration increases. The results perfectly duplicate the deformation features and stability condition of the physical model under the shake table test. It is shown that the presented method is more universal than the original one and can be adopted to evaluate the stability of the slope with potential toppling failure under seismic loads.
A Comparative Study of Feature Selection Methods for the Discriminative Analysis of Temporal Lobe Epilepsy
2017
Frontiers in Neurology
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It is crucial to differentiate patients with temporal lobe epilepsy (TLE) from the healthy population and determine abnormal brain regions in TLE. The cortical features and changes can reveal the unique anatomical patterns of brain regions from structural magnetic resonance (MR) images. In this study, structural MR images from 41 patients with left TLE, 34 patients with right TLE, and 58 normal controls (NC) were acquired, and four kinds of cortical measures, namely cortical thickness, cortical
doi:10.3389/fneur.2017.00633
pmid:29375459
pmcid:PMC5770628
fatcat:3xewjf4r6vcldoltsw4rrfj6fy
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... surface area, gray matter volume (GMV), and mean curvature, were explored for discriminative analysis. Three feature selection methods including the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE) were investigated to extract dominant features among the compared groups for classification using the support vector machine (SVM) classifier. The results showed that the SVM-RFE achieved the highest performance (most classifications with more than 84% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and GMV exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical measures were combined. Additionally, the dominant regions with higher classification weights were mainly located in the temporal and the frontal lobe, including the entorhinal cortex, rostral middle frontal, parahippocampal cortex, superior frontal, insula, and cuneus. This study concluded that the cortical features provided effective information for the recognition of abnormal anatomical patterns and the proposed methods had the potential to improve the clinical diagnosis of TLE.
Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images
2017
Frontiers in Aging Neuroscience
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Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative
doi:10.3389/fnagi.2017.00146
pmid:28572766
pmcid:PMC5435825
fatcat:txc3t7f5nvctpcjtaiv5p2t77e
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... ysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI-cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI-NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI-NC comparison. The best performances obtained by the SVM classifier using the essential features were 5-40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease. Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI-cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI-NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI-NC comparison. The best performances obtained by the SVM classifier using the essential features were 5-40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.
Numerical Studies on the Failure Process of Heterogeneous Brittle Rocks or Rock-Like Materials under Uniaxial Compression
2017
Materials
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Guo and Qi (2015) [75] used an index of stress concentration factor (SCF) that incorporated both the overall and local principal stress difference to denote the degree of heterogeneous stress (Equation ...
Guo and Qi (2015) [75] used an index of stress concentration factor (SCF) that incorporated both the overall and local principal stress difference to denote the degree of heterogeneous stress (Equation ...
doi:10.3390/ma10040378
pmid:28772738
pmcid:PMC5506975
fatcat:eir455ytrnfr3laav3wg67ljti
Progressive Failure Characteristics of Brittle Rock under High-Strain-Rate Compression Using the Bonded Particle Model
2020
Materials
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This paper microscopically investigated progressive failure characteristics of brittle rock under high-strain-rate compression using the bonded particle model (BPM). We considered the intact sample and the flawed sample loaded by split Hopkinson pressure bar respectively. Results showed that the progressive failure characteristics of the brittle rock highly depended on the strain rate. The intact sample first experienced in microcracking, then crack coalescing, and finally splitting into
doi:10.3390/ma13183943
pmid:32900005
fatcat:wgk5cxlkunb47ctmmjtkrefkey
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... ts. The total number of the micro cracks, the proportion of the shear cracks, the number of fragments and the strain at the peak stress all increased with the increasing strain rate. Also, a transition existed for the failure of the brittle rock from brittleness to ductility as the strain rate increased. For the flawed sample, the microcracking initiation position and the types of the formed macro cracks were influenced by the flaw angle in the initial stage. However, propagation of these early-formed macro cracks were prohibited in the later stages. New micro cracks were produced and then coalesced into diagonal macro cracks which could all form 'X'-shape failure configuration regardless of the incline angle of the flaw. We explored micromechanics on progressive failure characteristics of the brittle rock under dynamic loads.
Farming Influence on Physical-Mechanical Properties and Microstructural Characteristics of Backfilled Loess Farmland in Yan'an, China
2020
Sustainability
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A gigantic project named Gully Land Consolidation (GLC) was launched in the hill-gully region of the Chinese Loess Plateau in 2011 to cope with land degradation and create new farmlands for cultivation. However, as a particular kind of remolded loess, the newly created and backfilled farmland may bring new engineering and environmental problems because the soil structure was disturbed and destroyed. In this study, current situations and characteristics of GLC are introduced. Test results show
doi:10.3390/su12145516
fatcat:e6cyr32oovfobflijurgsrs6uu
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... at physical-mechanical properties and microstructural characteristics of backfilled loess of one-year and five-year farmland are significantly affected by the Gully Land Consolidation project. Compared to natural loess, the moisture content, density, and internal friction angle of backfilled loess increase. On the contrary, the porosity, plasticity index, particle size index, and cohesion index decrease. Through SEM tests, it is observed that the particles of backfilled loess are rounded, with large pores filled with crushed fine particles, which results in skeleton strength weakness among particles and pores. The pore size distribution (PSD) of the four types of loess (Q3 loess, Q2 loess, one-year farmland, and five-year farmland) was measured using mercury intrusion porosimetry (MIP) tests, showing that the pore size of Q3 loess is mainly mesopores 4000–20,000 nm in size, accounting for 67.5%. The Q2, five-year, and one-year farmland loess have mainly small pores 100–4000 nm in size, accounting for 52.5%, 51.7%, and 71.7%, respectively. The microscopic analysis shows that backfill action degrades the macropores and mesopores into small pores and micropores, leading to weak connection strength among soil particles, which further affects the physical-mechanical properties of loess. The disturbance of backfilled loess leads to an obvious decrease in cohesion and a slight increase in internal friction compared to natural loess. The farming effect becomes prominent with increased backfill time, while the loess soil moisture content increases gradually. Both the cohesion and internal friction of the backfilled loess soil decrease to different degrees. This study is helpful to investigate sustainable land use in the Chinese Loess Plateau and similar areas.
An Advanced Shear Strength Criterion for Rock Discontinuities Considering Size and Low Shear Rate
2020
Applied Sciences
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The shear strength of the rock discontinuities under different shear rates is of great importance to evaluate the stability of rock mass engineering, which is remarkably influenced by the size effects induced by both the length and the undulated amplitude of discontinuities. An advanced shear strength criterion taking into account the size and the shear rate simultaneously was proposed. There is an advantage of the dimension unity in terms of the new shear strength criterion in comparison to
doi:10.3390/app10124095
fatcat:qaqv3pn46ze23mzljktxygyfsy
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... vious related empirical equations. Additionally, it can be degraded into the International Society for Rock Mechanics (ISRM)-suggested Barton shear strength empirical equation on the peak shear strength of the rock discontinuities. Then, based on a new dynamic direct shear testing device on rock joints, the granite discontinuities with various lengths (200 mm to 1000 mm) and undulated amplitudes (3 mm to 23 mm) were designed to conduct direct shear tests under different low shear rates (0 mm/s to 1 mm/s) to verify the involved empirical equations. It was found that the results predicted by the new shear strength criterion agreed well with the experimental results. It was proved that the new shear strength criterion had a better applicability to characterize the shear strength of the rock discontinuities.
Load Rejection Transient Process Simulation of a Kaplan Turbine Model by Co-Adjusting Guide Vanes and Runner Blades
2018
Energies
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To obtain the flow mechanism of the transient characteristics of a Kaplan turbine, a three-dimensional (3-D) unsteady, incompressible flow simulation during load rejection was conducted using a computational fluid dynamics (CFD) method in this paper. The dynamic mesh and re-meshing methods were performed to simulate the closing process of the guide vanes and runner blades. The evolution of inner flow patterns and varying regularities of some parameters, such as the runner rotation speed, unit
doi:10.3390/en11123354
fatcat:wm7zrup6ener7mldk3imrla4nq
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... ow rate, unit torque, axial force, and static pressure of the monitored points were revealed, and the results were consistent with the experimental data. During the load rejection process, the guide vane closing behavior played a decisive role in changing the external characteristics and inner flow configurations. In this paper, the runner blades underwent a linear needle closure law and guide vanes operated according to a stage-closing law of "first fast, then slow," where the inflection point was t = 2.3 s. At the segment point of the guide vane closing curve, a water hammer occurs between guide vanes and a large quantity of vortices emerged in the runner and the draft tube. The pressure at the measurement points changes dramatically and the axial thrust rises sharply, marking a unique time in the transient process. Thus, the quality of a transient process could be effectively improved by properly setting the location of segmented point. This study conducted a dynamic simulation of co-adjustment of the guide vanes and the blades, and the results could be used in fault diagnosis of transient operations at hydropower plants.
Energy-Efficient Optimization of Two-Sided Disassembly Line Balance Considering Parallel Operation and Uncertain Using Multiobjective Flatworm Algorithm
2021
Sustainability
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The two-sided disassembly line is popular for its high-efficiency disassembly of large-volume end-of-life products. However, in the process of two-sided disassembly, some parts and components need to be disassembled in parallel, and the uncertainty of disassembly time lacks certain research. This paper constructs a fuzzy multiobjective two-sided disassembly line balance problem model based on parallel operation constraint, which aims to reduce the balance loss rate, smoothness index, and energy
doi:10.3390/su13063358
fatcat:3msgpo3uw5ba7ey2sv57jkz534
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... consumption of disassembly activities. A multiobjective flatworm algorithm based on the Pareto-dominance relationship is developed. To increase the diversity of feasible solutions in the evolution process and accelerate the convergence of Pareto-optimal solutions to prevent the random search of solution space, growth, splitting and regeneration mechanisms are embedded in the algorithm. The working mechanism and efficiency of the multiobjective flatworm algorithm are proved on a series of two-sided disassembly cases, and the excellent performance of the proposed model and algorithm are demonstrated by an actual automobile two-sided disassembly line.
A minimal path searching approach for active shape model (ASM)-based segmentation of the lung
2009
Medical Imaging 2009: Image Processing
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We are developing a minimal path searching method for active shape model (ASM)-based segmentation for detection of lung boundaries on digital radiographs. With the conventional ASM method, the position and shape parameters of the model points are iteratively refined and the target points are updated by the least Mahalanobis distance criterion. We propose an improved searching strategy that extends the searching points in a fan-shape region instead of along the normal direction. A minimal path
doi:10.1117/12.812575
pmid:24386531
pmcid:PMC3877238
dblp:conf/miip/GuoF09
fatcat:filwlkguhzhshea6kjj5wkca2y
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... P) deformable model is applied to drive the searching procedure. A statistical shape prior model is incorporated into the segmentation. In order to keep the smoothness of the shape, a smooth constraint is employed to the deformable model. To quantitatively assess the ASM-MP segmentation, we compare the automatic segmentation with manual segmentation for 72 lung digitized radiographs. The distance error between the ASM-MP and manual segmentation is 1.75 ± 0.33 pixels, while the error is 1.99 ± 0.45 pixels for the ASM. Our results demonstrate that our ASM-MP method can accurately segment the lung on digital radiographs.
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